U.S. patent number 8,685,093 [Application Number 12/358,430] was granted by the patent office on 2014-04-01 for methods and systems for diagnosing, treating, or tracking spinal disorders.
This patent grant is currently assigned to Warsaw Orthopedic, Inc.. The grantee listed for this patent is Kent M. Anderson, Thomas Carls, Eric C. Lange, Matthew M. Morrison. Invention is credited to Kent M. Anderson, Thomas Carls, Eric C. Lange, Matthew M. Morrison.
United States Patent |
8,685,093 |
Anderson , et al. |
April 1, 2014 |
Methods and systems for diagnosing, treating, or tracking spinal
disorders
Abstract
Methods and systems for performing a surgical procedure using
implantable sensors are disclosed. The method includes providing
one or more implantable sensors, each sensor configured for
implantation adjacent to an anatomical feature of a patient;
imaging the patient to determine the relative positions of the one
or more implantable sensors relative to the anatomical features of
the patient; inserting an implant adjacent to at least one of the
anatomical features; and tracking the position of the implant
relative to the at least one anatomical feature during the
inserting of the implant using the implantable sensors.
Inventors: |
Anderson; Kent M. (Sunnyvale,
CA), Morrison; Matthew M. (Cordova, TN), Carls;
Thomas (Memphis, TN), Lange; Eric C. (Pleasanton,
CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Anderson; Kent M.
Morrison; Matthew M.
Carls; Thomas
Lange; Eric C. |
Sunnyvale
Cordova
Memphis
Pleasanton |
CA
TN
TN
CA |
US
US
US
US |
|
|
Assignee: |
Warsaw Orthopedic, Inc.
(Warsaw, IN)
|
Family
ID: |
42354718 |
Appl.
No.: |
12/358,430 |
Filed: |
January 23, 2009 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20100191088 A1 |
Jul 29, 2010 |
|
Current U.S.
Class: |
623/17.11;
623/16.11; 600/424 |
Current CPC
Class: |
A61B
34/20 (20160201); A61B 17/7074 (20130101); A61B
5/686 (20130101); A61B 2090/0807 (20160201); A61B
5/11 (20130101); A61B 2034/2051 (20160201); A61B
2017/564 (20130101); A61B 2090/0812 (20160201); A61B
5/4504 (20130101); A61B 5/4519 (20130101); A61B
2034/105 (20160201); A61B 2034/108 (20160201); A61B
34/25 (20160201); A61B 5/4514 (20130101) |
Current International
Class: |
A61F
2/44 (20060101) |
Field of
Search: |
;623/17.11
;600/317,424 |
References Cited
[Referenced By]
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Other References
The Montreal Imaging and Orthopedics Research Laboratory; Focus on
The LIO and ARGOS; AROGS SpineNews, Apr. 2000, pp. 37-38. cited by
applicant .
Rapp, Susan M.; Ortho Supersite; Orthopedic Surgeons and
Rheumatologists only; Business of Orthopedics; Smart implants to
provide biofeedback, measure joint loads, detect infection;
Orthopedics Today 2008, (3 pages). cited by applicant .
Dumas, R., et al., Finite element simulation of spinal deformities
correction by in situ contouring technique, Computer Methods in
Biomechanics and Biomedical Engineering, vol. 8, Issue 5, pp.
331-337, Oct. 2005 (Abstract only, 1 page). cited by applicant
.
Breau, C., et al; BioMEMS and Biomedical Nanotechnology,
SpringerLink Journal, Dec. 6, 1990 (Abstract only,1 page). cited by
applicant .
Papin, P., et al.; Hand Transplantation, SpringerLink Journal,
(Abstract only, 2 pages). cited by applicant .
Eberlein, R., et al.; SpringerLink Journal, Apr. 2004 (Abstract
only, 1 page). cited by applicant .
Aubin, C.E., et al.; Deformable Models, SpringerLink Journal, Sep.
2003 (Abstract only, 1 page). cited by applicant .
Rajamani, K.T., et al.; Bone Model Morphing for Enhanced Surgical
Visualization; Biomedical Imaging: Nano to Macro, Apr. 2004, IEEE
International Symposium, (Summary only, 1 page). cited by applicant
.
Zebris, zebris Force Measuring Platform, The World of Biomechanics,
zebris Medical GmbH, Max-Eyth-Weg 42 (1 page). cited by applicant
.
Zebris, zebris Real Time Motion Analysis, The World of
Biomechanics, zebris Medical GmbH, Max-Eyth-Weg 42 (1 page). cited
by applicant.
|
Primary Examiner: Willse; David H
Assistant Examiner: Shipmon; Tiffany
Claims
What is claimed is:
1. A method of performing a surgical procedure to treat a condition
of a patient using implantable sensors, the method comprising:
providing one or more implantable sensors, each sensor configured
for implantation adjacent to an anatomical feature of the patient;
inserting one or more of the implantable sensors into the patient
adjacent to the anatomical feature using a minimally invasive
procedure; imaging the patient to determine the relative positions
of the one or more implantable sensors relative to the anatomical
features of the patient and to generate an image of the patient
including at least an image of the anatomical feature and the
implantable sensors; generating a model of the patient including at
least a model of the anatomical feature based on the generated
image of the patient and the determined relative positions of the
implantable sensors; tracking the positions of the implantable
sensors over time relative to the anatomical feature; selecting an
implant to treat the condition of the patient based on the tracking
of the implantable sensors; inserting the determined implant into
the patient adjacent to the anatomical feature using a minimally
invasive procedure, said implant including a sensor therein; and
tracking the position of the implant relative to the at least one
anatomical feature and the position of the sensor within the
implant relative to at least one of the implantable sensors during
insertion of the implant using the implantable sensors and the
generated image and model.
2. The method of claim 1, wherein at least one of the implantable
sensors comprises a housing having a bone engaging portion.
3. The method of claim 1, wherein one or more of the implantable
sensors inserted into the patient comprises an asymmetrical profile
such that an orientation of the sensor with respect to the adjacent
anatomical feature is detectable from the imaging.
4. The method of claim 1, wherein inserting the implant comprises
grasping the implant with a surgical tool, the surgical tool
including a sensor therein and wherein tracking the position of the
implant comprises tracking the relative position of the sensor
within the surgical tool to at least one of the implantable
sensors.
5. The method of claim 4, wherein inserting one or more of the
implantable sensors comprises positioning two or more sensors
symmetrical about the anatomical feature.
6. The method of claim 1, wherein the one or more sensors include a
housing having a bone engaging portion and an asymmetrical head
portion; and further comprising: engaging the bone engaging portion
of the housing with a vertebra; imaging the patient to determine
the relative position of the sensor relative to the vertebra using
the asymmetrical head portion of the housing as a guide; inserting
an implant adjacent to the vertebra; and tracking the position of
the implant relative to the vertebra by correlating the relative
position of the implant to the sensor to the vertebra.
7. The method of claim 1, wherein the implant is a bone screw
having a head, a threaded shaft and a neck positioned between the
head and the neck, the sensor within the implant being enclosed
within the shaft of the screw.
8. A method of performing a surgical procedure to treat a condition
of a patient using one or more sensors, the method comprising:
securing one or more sensors to the patient adjacent to an
anatomical feature of the patient; imaging the patient to determine
the relative positions of the one or more sensors relative to the
anatomical feature; tracking the positions of the sensors over time
relative to the anatomical feature; selecting an implant to treat
the condition of the patient based on the tracking of the sensors;
inserting an implant into the patient; and tracking the position of
the implant relative to the anatomical feature using the
sensors.
9. The method of claim 8, wherein the implant is a fixation
device.
10. The method of claim 8, wherein the tracking step comprises:
comparing the imaging of the patient to a previously planned
procedure; and positioning the implant at a previously planned
location based on the comparison.
11. The method of claim 8, wherein one or more of the sensors is
configured to identify from the imaging an orientation of the
sensor with respect to the anatomical feature.
12. The method of claim 8, wherein the implant includes a sensor
therein and wherein tracking the position of the implant comprises
tracking the relative position of the sensor within the implant to
at least one of the sensors.
13. The method of claim 8, wherein inserting the implant comprises
grasping the implant with a surgical tool, the surgical tool
including a sensor therein and wherein tracking the position of the
implant comprises tracking the relative position of the sensor
within the surgical tool to at least one of the sensors.
14. The method of claim 8, further comprising: post operatively
imaging the patient to determine the relative positions of the
implant, the anatomical feature, and the sensors.
15. The method of claim 8, further comprising: positioning the
implant in a final position; and removing the sensors after the
implant is positioned in the final position.
16. The method of claim 8, further comprising: providing on a
display a real-time display of the sensors, anatomical features and
implant.
17. A method of performing a surgical procedure to treat a
condition of a patient using at least one sensor, the method
comprising: positioning the at least one sensor within a housing
having a bone engaging portion, said sensor configured to identify
an orientation of the sensor during imaging; engaging the bone
engaging portion of the housing with a vertebra of the patient;
imaging the patient to determine the relative position of the
sensor relative to the vertebra using the orientation of the
sensor; tracking the positions of the sensor over time relative to
the vertebra; selecting an implant to treat the condition of the
patient based on the tracking of the sensor; inserting an implant
adjacent to the vertebra; and tracking the position of the implant
relative to the vertebra by correlating the relative position of
the implant to the sensor to the vertebra.
18. The method of claim 17, further comprising: positioning another
sensor within a housing having a bone engaging portion, said
another sensor configured to identify an orientation of the another
sensor during imaging; engaging the bone engaging portion of the
housing with another vertebra of the patient; imaging the patient
to determine a relative position of the vertebra to the another
vertebra using the orientations of the sensors; and using the
relative position of the vertebra to the another vertebra during
the insertion and tracking steps.
19. The method of claim 17, wherein the implant includes a sensor
therein and wherein tracking the position of the implant comprises
tracking the relative position of the sensor within the implant to
at least one of the sensors.
20. The method of claim 17, wherein inserting the implant comprises
grasping the implant with a surgical tool, the surgical tool
including a sensor therein and wherein tracking the position of the
implant comprises tracking the relative position of the sensor
within the surgical tool to at least one of the sensors.
Description
FIELD/BACKGROUND
The present disclosure is directed to improved systems and methods
for diagnosing, treating, and/or tracking medical conditions. More
particularly, in some aspects the present disclosure is directed to
systems and methods for diagnosing, treating, and/or tracking
spinal disorders.
In addition to the areas that are scientific in nature, a
significant portion of the practice of medicine is artistic in
nature. The medical professional studies at length the biology,
physiology and other disciplines related to his or her preferred
medical specialty or practice, and thereafter may reference the
scientific work of others for assistance. Further, he or she may
have specific data obtained from measurements or assessments of the
particular patient by way of x-rays, thermometers,
electrocardiograms, and/or other devices and machines. Even though
the data obtained may be undisputed, in many cases the root problem
may not be entirely clear. Further, even where the root problem is
clear there may be several possible treatment options. Accordingly,
the physician or other medical professional will rely on
experience, skill, and intuition to come to a conclusion as to what
the most effective treatment may be for the patient.
While decisions based on experience, skill, and intuition are
successful in many instances, in other instances these decisions
result in a course of treatment that is less effective than had
been hoped. In such cases, the patient may continue to be subjected
to discomfort during the less-effective treatment, or a condition
may worsen. Additionally, as medical procedures and devices become
more expensive and time-consuming, it becomes more important to
achieve a successful patient outcome in the first place from a
resource-conservation standpoint as well. Accordingly, there is a
need for improved devices, systems, and methods for diagnosing,
treating, and/or tracking medical problems. For example and without
limitation, there remains a need for improved devices, systems, and
methods for diagnosing, treating, and/or tracking spinal
disorders.
SUMMARY
The present disclosure provides devices, systems, and methods for
diagnosing, treating, and/or tracking medical conditions and, in
particular, spinal disorders.
In certain embodiments, a method of pathology assessment,
treatment, and outcome modeling is provided. The method includes
obtaining information from a patient concerning at least one of the
patient's characteristics, and defining one or more possible
therapeutic outcomes, thereby creating a plurality of therapeutic
factors, and weighting the factors. Accessing at least one database
having records of prior treatments of patients having similar
characteristics, pathologies, and/or therapeutic outcomes and
comparing the factors to information in the records. In at least
one embodiment, the most relevant of the records is identified
according to the weighted factors and at least a portion of each of
the records is retrieved from the database. In some instances, the
portion of the records obtained includes information regarding an
administered treatment plan. A simulation and/or outcome modeling
of each administered treatment from the records obtained is
performed to obtain a level of confidence in a particular outcome
resulting from said treatment. Based on the simulation and/or
outcome modeling a treatment plan for the current patient is
selected. The database includes information collected from one or
more medical treatment studies. In some instances, the medical
treatment studies include general spinal treatment and outcome
studies, spine trauma studies, lumbar spine studies, cervical spine
studies, spinal deformity studies, and/or other studies. In some
embodiments, the database also includes patient characteristic,
measurement, and pathology information, including information from
diagnostic tests. In some embodiments, some or all of the steps of
the method are performed electronically, such as over a computer
network. The selected treatment for the patient and its outcome are
provided to a database and/or a medical study in some instances. In
some embodiments, the prior treatments and the administered
treatments include spinal surgical procedures.
In another embodiment, a system for pathology assessment, treatment
and outcome modeling includes a database having a series of records
of patient treatments, the records including patient measurement
information, treatment information, and outcome information. In one
aspect, the system also includes at least one processor operatively
connected to the database and into which a set of information of a
current patient is entered and weighted. In some instances, the
processor is programmed to compare the current patient information
to the database information and to output information from records
in the database with similar information sets to the current
patient information. The outputted information includes treatment
information and outcome information. In some embodiments, the at
least one processor is programmed to simulate treatment options
and/or model outcomes. The processor is programmed for use with
item response theory models to compare said current patient
information to the database information in some embodiments. In
some instances, the processor is part of a computer or a computer
network and in some embodiments includes multiple processors at a
single or multiple locations.
In another embodiment, a method for pathology assessment, treatment
and outcome modeling includes obtaining a plurality of therapeutic
factors from a current patient, including information of at least
one of the patient's characteristics, the patient's pathology, and
one or more possible therapeutic outcomes, weighting the factors,
accessing at least one database having records of prior treatments
for patients having similar pathologies, comparing the factors to
information in the records, retrieving from one or more of the
records most relevant to the weighted factors, at least a portion
of each of the records, the portions including information
regarding the outcome of an administered treatment, and selecting a
treatment for the current patient based at least in part on said
outcome information. The weighting of the factors varies in some
instances based on the preferences of the practitioner, the
hypothesized pathology, experience, and/or other factors. The
treatment may be performed on the current patient and the database
may be updated with information regarding the patient's treatment
and outcome.
In another embodiment, a method for identifying available treatment
options for a patient having an increased likelihood of success is
provided. The method includes obtaining a plurality of therapeutic
factors from a current patient. The factors are based at least
partially on the current patient's physical characteristics,
pathology, and desired therapeutic outcomes. The method also
includes weighting the therapeutic factors and accessing at least
one database having records of prior patient treatments. The
records including prior patient therapeutic factors, treatment
plans, and treatment outcomes. The method also includes comparing
the therapeutic factors of the current patient with the prior
patient therapeutic factors in the records of the database to
identify prior patients with similar therapeutic factors and
retrieving from the database at least a portion of one or more
records of prior patients with similar therapeutic factors.
Finally, the method includes identifying available treatment
options for the current patient based at least in part on the
records of the prior patients with similar therapeutic factors.
In another embodiment, a system for identifying available treatment
options for a current patient having an increased likelihood of
success is provided. The system includes at least one local
database having a plurality of records of prior local patients. The
records of the prior local patients includes patient characteristic
information, treatment information, and outcome information. The
system also includes at least one remote database having a
plurality of records of prior remote patients. The records of the
prior remote patients includes patient characteristic information,
treatment information, and outcome information. The system also
includes at least one processing system operatively connected to
the local and remote databases. The at least one processing system
includes a diagnostic module, a modeling module, and a treatment
module. The diagnostic module is configured to receive and weight
current patient information, compare the current patient
information to the plurality of records of in the local and remote
databases, and retrieve records of prior patients with similar
characteristic information from the local and remote databases. The
treatment module is configured to identify available treatment
options for the current patient based at least partially on the
records retrieved from the local and remote databases by the
diagnostic module. The modeling module is configured to simulate
the available treatment options for the current patient identified
by the treatment module. The simulation is at least partially based
on the outcome information from the records of prior patients
retrieved from the local and remote databases.
In another embodiment, a method for identifying available treatment
options is provided. The method includes accessing at least one
database having records of prior patients. The records include
prior patient treatment plans and treatment outcomes. The method
also includes identifying prior patients with similar
characteristics to a current patient and retrieving from the
database at least a portion of the records of prior patients with
similar characteristics to the current patient. The portion of the
records retrieved includes the treatment plans and treatment
outcomes of the prior patients with similar characteristics.
Finally, the method includes identifying successful treatment plans
of prior patients based on the treatment outcomes.
In another embodiment, a method of obtaining and analyzing patient
information for diagnosis and treatment is provided. The method
includes identifying at least one patient symptom and selecting at
least one patient category associated with the at least one patient
symptom. The method also includes obtaining data corresponding to
the at least one patient category. The method also provides the
obtained data to a software application. The software application
analyzes the obtained data. The method also includes providing a
summary of the software application analysis for use in diagnosing
the patient's medical condition and identifying available treatment
options.
In another embodiment, a method of obtaining and analyzing patient
information for diagnosis and treatment is provided. The method
includes submitting a patient to diagnostic testing and obtaining
results from the diagnostic testing. The method also includes
categorizing the patient based on the results from the diagnostic
testing. The method also includes obtaining additional data
regarding the patient. In some instances, the additional data is
associated with the categorization of the patient. The method also
includes providing the obtained data and the results from the
diagnostic testing to a software application and analyzing the
obtained data and results from the diagnostic testing with the
software application. The method also includes identifying at least
one available treatment option for the patient based on the
analysis.
In another embodiment, a method of visualizing and analyzing
anatomical motion is provided. The method includes providing a
plurality of implantable sensors. Each of the plurality of
implantable sensors is configured for implantation adjacent to an
anatomical feature of a patient. The method also includes tracking
the positions of the implantable sensors as the patient is put
through a diagnostic motion protocol. The method also includes
correlating the positions of the implantable sensors to the
positions of the anatomical features of the patient adjacent to the
sensors. A motion sequence of the anatomical features is visualized
according to the positions of the anatomical features from the
diagnostic motion protocol. Finally, the method includes analyzing
the motion sequence of the anatomical features to identify a
medical problem.
In another embodiment, a system for visualizing and analyzing
anatomical motion is provided. The system includes a plurality of
implantable sensors. Each of the plurality of implantable sensors
is configured for implantation adjacent to an anatomical feature of
a patient. The system also includes a monitoring system in
communication with the implantable sensors. The monitoring system
is configured to track the positions of the sensors within the
patient during a diagnostic motion protocol. The system also
includes at least one processing system in communication with the
monitoring system. The at least one processing system includes a
modeling module configured to create an animated model of the
patient's anatomical features based at least partially on the
positions of the sensors as tracked by the monitoring system during
the diagnostic motion protocol. In some instances, a marker-less or
sensor-less tracking system is utilized. For example, in one
embodiment a plurality of cameras track the patient's motion from
different angles. The resultant images from the cameras are then
combined to create 3-D reconstructions of the motion, which are
then mapped to models of the patient's anatomical features.
In another embodiment, a method of performing a surgical procedure
using implantable sensors is provided. The method includes
providing one or more implantable sensors. Each of the sensors is
configured for implantation adjacent to an anatomical feature of a
patient. The method also includes imaging the patient to determine
the relative positions of the one or more implantable sensors
relative to the anatomical features of the patient. The method also
includes inserting an implant adjacent to at least one of the
anatomical features and tracking the position of the implant
relative to the at least one anatomical feature during the
inserting of the implant using the implantable sensors.
In another embodiment, a method of inserting a spinal implant is
disclosed. The method includes providing at least one sensor. The
at least one sensor is positioned within a housing having a bone
engaging portion and an asymmetrical head portion. The method also
includes engaging the bone engaging portion of the housing with a
vertebra. The patient is imaged to determine the relative position
of the sensor relative to the vertebra using the asymmetrical head
portion of the housing as a guide. The method also includes
inserting an implant adjacent to the vertebra. Finally, the method
includes tracking the position of the implant relative to the
vertebra by correlating the relative position of the implant to the
sensor to the vertebra.
In another embodiment, a method of selecting implant parameters is
provided. The method includes introducing one or more sensors
adjacent to an anatomical feature and monitoring a motion sequence
of the anatomical feature with the one or more sensors. The method
also includes analyzing the monitored motion sequence of the
anatomical feature to detect a problem in the motion sequence of
the anatomical feature. Finally, the method includes determining a
parameter for an implant for at least partially correcting the
problem in the motion sequence of the anatomical feature.
In another embodiment, a method of selecting a spinal implant and
its parameters is provided. The method includes introducing a
plurality of sensors adjacent to a pair of vertebrae defining a
spinal joint and monitoring a motion sequence of the spinal joint
with the plurality of sensors. The method also includes analyzing
the monitored motion sequence of the vertebrae to detect an initial
problem in the motion sequence of the spinal joint. The method
includes determining a parameter for an implant for correcting the
initial problem in the motion sequence of the spinal joint.
Finally, the method also includes identifying at least one spinal
implant with the parameter for correcting the initial problem in
the motion sequence of the spinal joint.
In another embodiment, a method of detecting implant loosening is
provided. The method includes providing an implant for fixedly
engaging with an anatomical feature of a patient. The implant has a
first sensor secured thereto. The method also includes tracking a
first motion pattern of the first sensor and tracking a second
motion pattern of a second sensor secured to the anatomical
feature. The method also includes determining a relative motion
between the first sensor and the second sensor based on the first
and second motion patterns. Finally, the method includes
identifying implant loosening by analyzing the relative motion
between the first sensor and the second sensor.
In another embodiment, a method of detecting implant loosening is
provided. The method includes inserting a first sensor into a bone
structure and securing the first sensor in a fixed position with
respect to the bone structure. The method also includes engaging an
implant with at least a portion of the bone structure. The implant
has a second sensor positioned therein. The method also includes
securing the implant with the portion of the bone structure such
that the second sensor is substantially fixed with respect to the
bone structure and the first sensor. Finally, the method includes
monitoring the position of the second sensor with respect to the
first sensor to identify implant loosening.
Further aspects, forms, embodiments, objects, features, benefits,
and advantages of the present disclosure shall become apparent from
the detailed drawings and descriptions provided herein.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a diagrammatic schematic view of a system for use in
treating a patient according to one embodiment of the present
disclosure.
FIG. 2 is a flow chart illustrating a method for diagnosing,
treating, and monitoring a patient according to another embodiment
of the present disclosure.
FIG. 3 is a flow chart illustrating the evaluation step of the
method of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 4 is an exemplary screen shot of a software interface that is
utilized as part of the evaluation step of the method of FIG. 2
according to one embodiment of the present disclosure.
FIG. 5 is a flow chart illustrating the imaging step of the method
of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 6 is a flow chart illustrating the patient analysis step of
the method of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 7 is a flow chart illustrating the identification step of the
method of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 8 is an exemplary screen shot of a software interface that is
utilized as part of the identification step of the method of FIG. 2
according to one embodiment of the present disclosure.
FIG. 9 is a flow chart illustrating the modeling step of the method
of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 10 is an exemplary screen shot of a software interface showing
a representative figure of the modeling step of the method of FIG.
2 according to one embodiment of the present disclosure.
FIG. 11 is a flow chart illustrating the selection step of the
method of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 12 is an exemplary screen shot of a software interface that is
utilized as part of the selection step of the method of FIG. 2
according to one embodiment of the present disclosure.
FIG. 13 is a flow chart illustrating the planning step of the
method of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 14 is a flow chart illustrating the performance step of the
method of FIG. 2 according to one embodiment of the present
disclosure.
FIG. 15 is a flow chart illustrating the post-treatment analysis
step of the method of FIG. 2 according to one embodiment of the
present disclosure.
FIG. 16 is a diagrammatic schematic view of a node for implementing
the systems and methods of the present disclosure according to one
embodiment of the present disclosure.
FIG. 17 is a flow chart of a data flow method according to another
aspect of the present disclosure.
FIG. 18 is a flow chart illustrating a patient diagnostic modeling
method according to another aspect of the present disclosure
FIG. 19 is a diagrammatic schematic view of a system for performing
the methods disclosed herein according to one aspect of the present
disclosure
FIG. 20 is a flow chart illustrating a method of collecting and
assessing data associated with a method for diagnosing a patient
and selecting available treatment options for the patient according
to another embodiment of the present disclosure.
FIG. 21 is a flow chart illustrating a method for diagnosing a
patient, identifying available treatment options for the patient,
selecting a treatment option for the patient, and performing the
selected treatment option according to another embodiment of the
present disclosure.
FIG. 22 is a diagrammatic schematic view of a data structure for
use with the methods of FIGS. 20 and 21 according to one embodiment
of the present disclosure.
FIG. 23 is a flow chart illustrating a method for visualizing and
analyzing anatomical motion according to one embodiment of the
present disclosure.
FIG. 24 is a flow chart illustrating a method for using implantable
sensors in an image-guided treatment according to one embodiment of
the present disclosure.
FIG. 25 is a diagrammatic partial cross-sectional side view of a
bone screw in accordance with one embodiment of the present
disclosure.
FIG. 26 is a diagrammatic top view of the bone screw of FIG.
25.
FIG. 27 is a diagrammatic top view of a bone screw similar to that
shown in FIG. 26 but illustrating an alternative embodiment of the
present disclosure.
FIG. 28 is a diagrammatic side view of a system according to
another embodiment of the present disclosure for using sensors in
an image-guided treatment.
FIG. 29 is flow chart illustrating a method for selecting and
modifying implant parameters using implanted sensors according to
one embodiment of the present disclosure
FIG. 30 is a diagrammatic side view of a bone anchor having a
plurality of sensors therein according to one embodiment of the
present disclosure.
FIG. 31 is a diagrammatic side cross-sectional view of the bone
anchor of FIG. 31.
FIG. 32 is a diagrammatic side view of a system for monitoring
implant loosening according to one embodiment of the present
disclosure.
FIG. 33 is a diagrammatic cross-sectional view of a system for
monitoring implant loosening according to another embodiment of the
present disclosure.
DETAILED DESCRIPTION
For the purposes of promoting an understanding of the principles of
the present disclosure, reference will now be made to the
embodiments illustrated in the drawings, and specific language will
be used to describe the same. It will nevertheless be understood
that no limitation of the scope of the disclosure is intended. Any
alterations and further modifications in the described devices,
instruments, methods, and any further application of the principles
of the disclosure as described herein are contemplated as would
normally occur to one skilled in the art to which the disclosure
relates. In particular, it is fully contemplated that the features,
components, and/or steps described with respect to one embodiment
may be combined with the features, components, and/or steps
described with respect to other embodiments of the present
disclosure.
Referring to FIG. 1, shown therein is a diagrammatic schematic view
of a system 10 for use in treating a patient according to a first
embodiment of the present disclosure. Among other aspects, the
system 10 is used by medical professionals and other medical
personnel for assisting them in making decisions on treatments
aimed at the desired clinical outcomes for the patient. The system
10 includes methods for coordinating a number of sources of data
and assimilating the relevant data to provide actionable
information for the medical personnel. Using such information with
the appropriate confidence intervals, the medical personnel develop
a clinical decision on an appropriate treatment plan based on data
of actual patient outcomes relating to identical or similar
treatments. In particular embodiments, the data analysis takes the
form of an item response theory (IRT) modelization technique that
allows medical personnel to compare the patient's symptoms to
previous patients having similar symptoms, and then identify the
treatments and corresponding outcomes for the previously treated
patients. The previous patient outcomes are conditioned or weighted
based on a probabilistic treatment result. Based on the available
data, the system 10 objectively decides one or more of the variable
or "trade-off" decisions typically performed by skilled
professionals that are used to select among a plurality of
different treatment options.
In some instances, the system 10 is continuously optimized by
tracking patient outcomes with the corresponding treatment plans
and modifying treatment plans for future patients accordingly. In
that regard, in some instances the system 10 utilizes fuzzy logic
and/or genetic algorithms for correlating future patient symptoms
with prior patient symptoms and outcomes to identify the best
treatment plan for the patient based on desired outcomes. In that
regard, patient outcome goals may include, without limitation, a
particular Oswestry disability form and score (ODI), overall
patient satisfaction with treatment, reduction or elimination of
symptoms (e.g., pain, limited mobility, etc.), improved score on
Neck Disability Index (NDI), improved SF-36 (Short Form 36) score,
improved HRQL (Health Related Quality of Life) score, restoration
or improvement of quality of life, and/or other factors. More
particularly, where surgical treatments are undertaken the outcome
goals may include, without limitation, restoration or improvement
of mobility, restoration or improvement of range of motion,
restoration or improvement of balance in the sagittal and/or
coronal planes, restoration or improvement of center of gravity,
preservation of healthy anatomy (ligaments, cartilage, bony
anatomy, etc.), restoration or improvement of gait, and/or other
factors. In addition to the patient specific surgical outcome
goals, the present disclosure provides methods and systems for
improving aspects of surgical procedures in general including,
without limitation, improved cosmesis (particularly muscular and
skin incision in some instances), reduced harvest morbidity,
reduced need for harvests (BMP), reduced morbidity, reduced
complications, reduced cost of surgery, reduced time in operating
room, reduced recovery time, reduced blood loss, reduced likelihood
of revision surgery, reduced likelihood of adjacent level disc
disease, reduced adjacent organ restrictions or impairment (e.g.,
lung) caused by trauma or extreme deformity, and/or other
factors.
While the systems and methods disclosed herein will be described in
the context of orthopedic treatments and, in particular, with
surgical orthopedic treatments of the spine, it is understood that
systems and/or methods similar to those described herein are useful
for diagnosing and treating patients in numerous other medical
fields, including but not limited to cardiology and oncology.
Further, while the treatment plans discussed herein will be focused
on surgical procedures, the treatment plans also include
non-invasive treatments, such as physical therapy protocols, in
addition to or in lieu of the surgical procedures in some
embodiments. Similarly, the treatment plans also include medicinal
treatments as well in some embodiments.
Referring again to FIG. 1, the system 10 includes a diagnosis
module 12, a modeling module 14, a treatment module 16, and a
post-treatment feedback module 18. In some embodiments, the system
10 is a spinal disorder diagnostic and treatment system. In that
regard, the system 10 is utilized by physicians, surgeons, medical
assistants, and/or other medical personnel to diagnose motion
disorders, model patient-specific treatment plans, plan and deliver
treatment to the patient, acquire feedback regarding the
effectiveness of the treatment, modify the treatment as needed,
track patient results based on treatment plans, and/or continuously
improve patient treatment by correlating successful treatment plans
with specific patient symptoms or characteristics related to the
motion disorders. As described below, the system 10 provides
communication between medical personnel (physicians, surgeons,
therapists, medical assistants, etc.), patients, and/or medical
device manufacturers. Also, the system 10 is particularly adapted
for providing a platform for executing the methods described below.
Additional details regarding the modules 12, 14, 16, and 18 of the
system 10 will be described in more detail below in relation to
these methods. Other uses of the system 10 and its modules 12, 14,
16, and 18 will be apparent to one skilled in the art from the
following description and should be considered part of the present
disclosure.
Referring to FIG. 2, shown therein is a flow chart illustrating a
method 20 for diagnosing, treating, and monitoring a patient
according to another aspect of the present disclosure. The method
20 begins at step 22 with evaluation of the patient. The evaluation
determines whether the patient should be subjected to the
subsequent steps of the method 20. In that regard, the evaluation
may vary depending on the types of treatments contemplated by the
method 20. For example, in some embodiments the method 20 is based
predominantly on surgical treatments. In such embodiments, the
evaluation at step 22 focuses on determining whether the patient is
a potential candidate for the available surgical treatments. If the
evaluation at step 22 indicates that the patient is not a candidate
for the available surgical treatments (e.g., due to age or other
factors), then the subsequent steps of the method 20 are not
performed. On the other hand, if the patient has a condition or
symptom that indicates that the patient is definitely a candidate
for the available surgical treatments (e.g., spondylolisthesis of
grade 2 or more), then the evaluation step may either be truncated
or completely skipped, and the method 20 may continue with the
subsequent steps.
Referring more specifically to FIG. 3, shown therein is a flow
chart illustrating the evaluation step 22 of the method 20
according to one embodiment of the present disclosure. Generally,
the evaluation at step 22 will focus on determining whether the
patient is a candidate for the contemplated treatment options. For
example, in the context of spinal disorders the method 20 provides
treatment options that include spinal implants and related surgical
techniques for correcting the spinal disorder. The evaluation step
22 includes obtaining information from the patient. Such
information may include standard information on the physical
characteristics and condition of the patient. In a particular
embodiment, such information includes a series of options for the
patient and/or the medical professional to select. Such options are
geared toward helping to determine an appropriate treatment, as
disclosed herein, and also include goals as to post-operative
mobility, activity, or relative deformity; pre-operative condition;
prior surgeries or other treatments; or other factors. In the
current embodiment, at least some information is obtained by
determining the answers to a series of diagnostic questions in step
78. The diagnostic questions include questions such as: How far can
the patient walk without pain? Does the patient have pain lying
down? Does the patient have pain sitting? Does the patient have
pain standing? Does the patient have back pain with leg pain? If
yes, is the leg pain localized or radiating? These are exemplary
questions and are not to be considered limiting. Numerous other
questions may be utilized to evaluate the patient. In that regard,
the questions are nested such that subsequent questions depend on
the answers to previous questions. Further, some or all of the
information provided is weighted so as to emphasize one or more
factors as the analysis of potential treatments is performed in
some instances. That is, answers to particular questions or
information provided are given more importance than other answers
or information.
In some embodiments, the questions of step 78 are provided to the
patient and/or medical personnel in an interactive computer
program. In some embodiments, the diagnosis module 12 of the system
10 (FIG. 1) prompts a user to answer the series of diagnostic
questions and/or provide menus for selecting items indicative of
the patient's medical condition. Referring to FIG. 4, shown therein
is an exemplary screen shot 40 of a software interface that is
utilized as part of the evaluation at step 22. A spinal disorder
selection menu 42 is provided on the left hand side of the screen
shot 40. The menu 42 provides a drop down menu containing a
plurality of spinal disorders. The treating physician or other
medical personnel selects the spinal disorder(s) afflicting the
patient using the menu 42. In other embodiments, the menu 42
includes symptoms (e.g., low back pain, limited flexion, etc.)
instead of or in addition to the spinal disorders in some
embodiments. A note field 44 is provided on the right hand side of
the screen shot 40 allowing additional information regarding the
patient to be recorded. It is understood that the screen shot 40
represents a single part of the evaluation step 22 and is not to be
considered limiting. In that regard, the evaluation step 22
includes one or more additional pages or screen shots containing
additional questions, menus, and/or other inputs related to the
patient's condition in some embodiments.
Referring again to FIG. 3, in addition to or in lieu of the
diagnostic questions 78 the evaluation at step 22 includes other
types of patient analysis. In the current embodiment, imaging
techniques are utilized to evaluate the patient at step 80. For
example, in some embodiments radiographic images of the patient's
anatomy are obtained. The radiographic images are then analyzed to
identify any medical conditions afflicting the patient. The medical
conditions are then considered as a factor in evaluating the
patient. In some embodiments, the patient is put through a series
of movements appropriate to determine the patient's motion sequence
and/or range of motion for one or more anatomical areas. The
patient's motion sequence and/or range of motion in each area is
then considered as a factor in evaluating the patient. Additional
considerations and/or tests are taken into account during the
evaluation of the patient as desired by the treating physician or
other medical personnel.
Based on the response to the diagnostic questions 78, imaging data
80, and/or other types of patient analysis, the patient can be
grouped into a classification at step 82. In some embodiments, the
classification 82 is by type of injury or medical condition. In
other embodiments, the classification 82 is based on other patient
factors. In some embodiments, the classification 82 is at least
partially based on a treating physician or other medical
personnel's preferences. It is contemplated that, in some
embodiments, each classification is further subdivided into groups
based on factors such as the severity of the condition, age,
health, and/or other factors. In some embodiments, the
classifications and groupings are based on factors identified in
clinical studies and/or past patient treatments as being indicators
of success for the available treatment options. A general
determination can be made regarding whether the patient is a
candidate for the available treatment options based on the grouping
and classifications. In that regard, it is contemplated that each
classification or grouping defines an inclusion group that
indicates that the patient is a candidate for an available group of
treatment options. If the patient is not a candidate for the
available treatment options then the method 20 terminates. If,
however, it is determined that the patient is a candidate for the
available treatment options, then the method 20 continues with step
24.
At step 24, the patient is subjected to an imaging study. Referring
more specifically to FIG. 5, shown therein is a flow chart
illustrating the imaging step 24 of the method 20 according to one
embodiment of the present disclosure. The imaging study includes
obtaining patient images through the use of magnetic resonance
imaging ("MRI"), computed tomography ("CT"), video fluoroscopy,
and/or other imaging techniques at step 84. In some embodiments,
the imaging study includes techniques as described in commonly
owned U.S. patent application Ser. No. 11/697,426 filed Apr. 6,
2007 and titled "System and Method for Patient Balance and Position
Analysis", herein incorporated by reference in its entirety. In
general, the imaging study obtains images of the patient's anatomy
that are utilized in subsequent steps of the method 20. In
particular, the imaging study of step 24 focuses on obtaining
images and/or information necessary to model portions of the
patient's anatomy.
In some embodiments, the imaging study of step 24 includes tracking
the movement of anatomical features of the patient using sensors.
In some embodiments the sensors are implantable and are placed in
direct contact with and/or within the relevant anatomical
feature(s) of the patient. In other embodiments, the sensors remain
outside of the patient's body, but are positioned in close
proximity to the anatomical feature(s) of interest. For example, in
some embodiments the imaging study tracks the position of at least
some of a patient's vertebrae. In one embodiment, a sensor is
implanted into each vertebra and the location of the vertebra is
tracked using the sensor. In another embodiment, a sensor is placed
outside the patient's body adjacent the spinous process. The
location of the spinous process and, in turn, the vertebra are
tracked using the sensor. In some embodiments, the position of the
sensors and anatomical features are tracked while the patient is
put through a particular motion sequence or protocol. For example,
in one embodiment the patient is asked to walk on a treadmill. The
position of the sensors and anatomical features are tracked and
correlated to the patient's gait cycle. It is understood that these
described uses of sensors are merely exemplary and should not be
considered limiting. Sensors, implantable or otherwise, may be
utilized in numerous other combinations and ways to track the
position of anatomical features during the imaging study.
The data from the imaging study is provided to one or more software
applications at step 86 in order to derive further information
and/or new views of the imaging data. Generally suitable software
packages will be capable of one or more of 2-D radiographic
measurement and analysis; 3-D modeling, reconstruction, and
kinematic simulation; therapy modeling or simulation; and outcome
simulation. Examples of such software include the Montreal 3D
Radiographic Modeling, Measurement and Surgery Simulation software
("Montreal software"); the TruBalance patient measurement software
("TruBalance software"); and the DRPro radiographic measurement
software offered by PhDx eSystems, Inc. of Albuquerque. Other
brands or types of software for obtaining, analyzing, or otherwise
handling patient data may be used in addition to or instead of one
or more of the software applications mentioned above for one or
more of the data categories. Also, multiple software applications
may be applied to a given set of data. It is understood that data
from each study can be assembled together prior to submission to
such software, or each study can be treated individually.
In some embodiments, the Montreal software is used to generate a
three-dimensional model of the patient's spine, the TruBalance
software is used to calculate a global balance for the patient, and
the DRPro software is used to measure the images. In some
embodiments, an additional step that can be used is to measure the
images with software known as Clindexia. At step 88, these software
applications transform the raw images into mathematical or other
forms that can be manipulated via a computer system and compared to
other images and/or other data sets.
Referring to FIGS. 2 and 6, after the imaging study of step 24, the
method 20 continues with step 26 in which a patient analysis is
performed. Referring more particularly to FIG. 6, shown therein is
a flow chart illustrating the patient analysis step 26 of the
method 20 according to one embodiment of the present disclosure.
Generally, the patient analysis of step 26 synthesizes the
information obtained during steps 22 and 24 to identify the
abnormal medical conditions afflicting the patient. In that regard,
in the current embodiment step 26 begins with retrieving the
patient evaluation and/or imaging study data from steps 22 and 24
at step 90. The patient analysis step 26 continues with step 92 in
which a 3-D and/or 2-D animated model of the patient's anatomy is
created. Generally, the animated model is based on the data
obtained from the imaging study of step 24. In some embodiments,
the animated model is used to highlight the problem areas and/or
times in the patient's anatomical motion sequence or motion
pattern. In that regard, motion sequences and/or motion patterns as
the terms are used herein are intended to include a patient's gait,
a portion of the patient's gait, a single movement of a single
anatomical structure, a series of movements of a single anatomical
structure, a single movement of a plurality of anatomical
structures, a series of movements of a plurality of anatomical
structures, or other aspects of a patient's motion. Generally, any
patient motion in whole or part may be referred to as a motion
sequence or motion pattern.
The model of the patient's anatomy includes layers of anatomical
features that are selectively included or removed. For example, in
one embodiment the patient's motion anatomy is grouped into layers
according to types of anatomical tissue, such as bones, cartilage,
ligaments, tendons, muscles, and/or combinations thereof. The
animated model then analyzes motion according to each grouping of
anatomical tissue and the interactions therebetween.
In some embodiments, the animated model combines diagnostic tests
with the imaging study. For example, in some embodiments the
animated model combines muscle monitoring with the imaging study to
identify muscle contractions and tensions during a motion sequence
or protocol. The results of the muscle monitoring are combined with
the other imaging data to provide additional details and/or realism
to the animated model. In other embodiments, the animated model
utilizes center-of-balance or center-of-gravity data for the
patient obtained during the motion sequence or protocol. Muscle
monitoring and center-of-balance data are merely examples of the
types of additional data that may be combined with the imaging data
in forming the animated model. Other types of the patient data may
also be utilized. In that regard, in some embodiments the treating
physician or medical personnel selects the types of patient data to
be used in formulating the animated model.
The animated model includes additional features to allow medical
personnel and/or a computer system to analyze the patient. In that
regard, in some embodiments the animated model includes a stress
grid overlay that indicates potential areas of increased stress or
strain on the patient's anatomy, such as increased muscle activity;
overstretching of muscles, ligaments, and/or tendons; friction
between bones; and/or other areas of stress/strain. In some
embodiments the model allows for zooming, panning, or otherwise
changing the orientation of the view of the patient's anatomy. A
user adjusts the orientation to better observe or isolate a
potential problem area. Similarly, the animated model allows a user
to pause, rewind, slow down, and/or speed up simulation of a motion
sequence to better observe a potential problem. Further, the
animated model allows 3-D and/or 2D tracking of specific anatomical
features through the motion sequences. At step 94, the animated
model highlights potential problem areas automatically based on a
comparison to a standardized model associated with the patient
and/or the treating physician or medical personnel highlights
potential problem areas based on their observations. In some
embodiments, the problem areas are identified by a computer system
and/or medical personnel by recognizing an abnormal motion
pattern(s).
At step 96, a statistical summary of the patient analysis is
provided. The summary provides information important to the
diagnosis and subsequent treatment of the patient's medical
condition. In some embodiments, the statistical summary identifies
such things as damaged anatomical features or areas, limited ranges
of motion, and/or other data related to the patient's condition.
The information provided is at least partially determined by the
medical personnel. For example, in some embodiments the medical
personnel selects or otherwise accesses the particular information
or data sets they deem to be most important in diagnosing and
treating the patient. In some embodiments, the statistical summary
provides a comparison to other patients with similar medical
conditions, medical histories, and/or patient profiles. Further,
the selected treatment plans and relative success of those plans
for the other patients is provided. The statistical summary also
provides a list of possible causes for the medical condition and/or
identifies possible relationships between abnormal motion
patterns.
Referring to FIGS. 2, 7, and 8, after the patient analysis of step
26, the method 20 continues with step 28 in which the available
treatment options are identified. The treatment options are based
upon the patient analysis. Referring more particularly to FIG. 7,
shown therein is a flow chart illustrating the identification step
28 of the method 20 according to one embodiment of the present
disclosure. In this particular embodiment, the treatment options
are determined by looking at the statistical summary of the patient
analysis at step 98, identifying the patient's medical condition(s)
at step 100, and proposing treatment plans based on the patient's
medical condition(s) at step 102. The proposed treatment plans
include surgical procedures, non-invasive treatments, and/or
medicinal treatments. For sake of example and simplicity, a series
of proposed surgical treatment plans will now be discussed in the
context of a disc herniation in the lumbar region of the spine as
identified by a patient analysis. This is for exemplary purposes
only and should not be considered limiting in any way.
Referring more particularly to FIG. 8, shown therein is an
exemplary screen shot 46 of a software interface that is utilized
as part of identifying the available treatment options at step 28.
In the current embodiment, a spinal disorder menu 48 is provided on
the left hand side of the screen shot 46. The spinal disorder menu
48 currently indicates that the patient suffers from a lumbar disc
herniation. In other instances, the patient may suffer from other
spinal disorders and/or a plurality of spinal disorders. A
treatment menu 50 is provided on the right hand side of the screen
shot 46 and includes a plurality of treatment plans. The treating
physician or other medical personnel selects one or more of the
treatment plans from among the plurality of treatment plans using
the treatment menu 50. In the current embodiment, the treatment
menu 50 provides a plurality of surgical treatment options for
correcting a lumbar disc herniation. In some embodiments, the
treatment menu 50 includes an option allowing a surgeon or other
physician to input a treatment plan based on her own experience
that is not included in the plurality of treatment options.
Referring again to FIGS. 2, 7, and 8, in some embodiments the
treatment options of step 28 are sorted and/or screened based on
physician preference at step 104. For example, if a physician
prefers surgical procedures that utilize a posterior approach, then
the available treatment options are limited to those implants and
surgical procedures that are implanted through a posterior
approach. As another example, the treatment options are sorted
based on the success of the treatment plan for previous patients
having a similar profile to the current patient. Similarly, in some
embodiments the treatment options are sorted based on the previous
procedures performed by the treating physician/surgeon and the
relative success of those procedures. In other embodiments, the
patient suffers from medical conditions unrelated to the spine that
is presented in a similar manner--indicating the medical condition
and proposing a plurality of treatment options.
Referring to FIGS. 2, 9, and 10, after identifying one or more of
the available treatment plans at step 28, the method 20 continues
at step 30 with modeling of the available treatment options.
Referring more specifically to FIG. 9, shown therein is a flow
chart illustrating the modeling step 30 of the method 20 according
to one embodiment of the present disclosure. Modeling of the
treatment options builds upon the animated model of the patient
analysis of step 26. In that regard, the modeling step 30 begins
with retrieving the model of the patient's anatomy at step 106.
Next, the modeling step 30 continues by modifying the 3-D and/or
2-D animated model of the patient's anatomy according to the
treatment plan at step 108. For example, in some embodiments the
animated model is modified by replacing a damaged portion of the
patient's anatomy with an implant. A model can then be created
utilizing the characteristics of the implant in place of the
damaged portion of the patient's anatomy as indicated by step 108.
Referring to FIG. 10, shown therein is a screen shot 52 of a
software interface showing a representative figure of a modeling
according the present embodiment.
In some embodiments, the modeling is used to identify potential
problem areas and/or times in the patient's anatomical motion
sequence that remain after implantation of the implant at step 110.
In that regard, as previously described the model includes layers
of anatomical features that may be selectively included or removed,
such as bones, cartilage, ligaments, tendons, muscles, and/or
combinations thereof. The model analyzes the motion sequence at
each level of anatomical tissue with the implant in place and then
the model the resultant motion sequence including all of the
levels. In that regard, in some embodiments the model takes into
account the surgical procedure or approach utilized in inserting
the implant. For example, if muscles, tendons, cartilage, and/or
other supporting tissues will be cut or resected during the
surgical procedure, then the model takes this into account in
modeling the resultant motion sequences. The model highlights
potential problem areas automatically based on a comparison to a
standardized model associated with the patient and/or the treating
physician or medical personnel may highlight potential problem
areas based on their observations of the resultant motion sequence.
In some embodiments, the problem areas are identified by a computer
system and/or medical personnel by recognizing or tracking an
abnormal motion pattern(s).
By identifying potential problem areas and/or times in the
patient's anatomical motion sequence and taking into account the
tissues that will be compromised during the surgical procedure, the
modeling provides a realistic estimation of the resultant outcome
of the treatment plan. In that regard, the treating physician
optimizes each treatment plan by modifying such factors as the
size, placement, orientation, and material properties of a
particular implant and/or modifying the surgical procedure to
adjust the tissues that will be compromised at step 112. Further,
the treatment plan is modified according to weighted factors
concerning the patient's characteristics and/or the desired outcome
at step 112. After the treatment plan is modified the modeling step
30 may return to step 108 and update the model according to the
modified treatment plan. This process may be iterated until the
physician is satisfied with the parameters of the treatment plan.
For each of the selected treatment plans and/or implants, the
treating physician saves one or more optimized plans in a database
or other accessible memory location. A statistical summary of the
optimized treatment plan is provided for each selected treatment
plan at step 114.
Additional features as previously mentioned may be utilized to
model the treatment plans. In some embodiments the model includes a
stress grid overlay that indicates potential areas of increased
stress or strain on the patient's anatomy, such increased muscle
activity; overstretching of muscles, ligaments, and/or tendons;
friction between bones; and/or other areas of stress/strain caused
by the implant and/or treatment plan. In some embodiments the model
allows for zooming, panning, or otherwise changing the orientation
of the view of the patient's anatomy with the implant inserted. A
user adjusts the orientation to better observe placement and/or
functioning of the implant. Similarly, the model allows a user to
pause, rewind, slow down, and/or speed up simulation of a motion
sequence to better observe the patient's motion with the implant.
Further, the model allows 3-D and/or 2D tracking of specific
anatomical features through the motion sequences.
In some embodiments, the method 20 does not include step 30. In
other embodiments, the method 20 includes the modeling and
optimization of step 30 with respect to only some of the selected
treatment plans. In that regard, some treatment plans do not lend
themselves to modeling and, therefore, may not be modeled even when
other selected treatment plans are modeled. Further, the example
described above focused on treatment plans including insertion of
implant and the corresponding surgical procedures. It is understood
that similar modeling and optimization approaches are utilized to
model non-surgical procedures and/or other types of treatment plans
in some embodiments.
Referring to FIGS. 2, 11, and 12, after optimizing each of the
selected treatment plans at step 30, the method 20 continues at
step 32 where a treatment option is selected. Referring more
particularly to FIG. 11, shown therein is a flow chart illustrating
the treatment plan selection step 32 of the method 20 according to
one embodiment of the present disclosure. Generally, a physician
and/or a computer system compares the modeled results and/or
statistical summaries for each of the optimized plans and selects
the plan best suited for correcting the patient's medical
condition. The selection step 32 begins with retrieving the
statistical summaries of the available treatment options at step
116. The plan best suited for the patient is based on such factors
as the patient's profile, the desired results, the physician's
preferences, and/or the patient's preferences. It is contemplated
that in some instances a computer system ranks the treatment
options based on the results of previous patients having similar
profiles to the current patient. In that regard, the computer
system includes the confidence level for particular outcomes for
each treatment option in some embodiments. Accordingly, at step 118
the statistical summaries of the available treatment options are
compared to the desired patient outcomes. With the clinical
outcomes modeled and the results displayed with respective
confidence intervals or levels for each outcome related to the
particular treatment options, medical personnel can make the
appropriate decisions for treating the patient with by balancing
the trade-off of parameters that important to the medical personnel
and the patient's outcome. The medical personnel's decision can be
made from actual patient data relative to a similar condition that
represents their particular patient's problem. In the end, taking
all of the various considerations into account the best available
treatment option for the patient is selected at step 120.
In addition to selecting a treatment option, step 32 also includes
discussing the selected treatment option with the patient at step
122. In that regard, the results of the analyses and modeling are
shown and/or explained to the patient to support the decision to go
with a particular treatment. Further, in the case of a treatment
plan that includes inserting an implant or otherwise employing a
medical device, the patient may be given access to additional
product information regarding the medical device. In some
embodiments, discussing the treatment option with the patient is
accomplished over the internet, an intranet, computer network,
telecommunications network, or other type of remote connection. In
that regard, the link between the patient and the medical
professional may be a secure link or secured communication channel
so as to protect the patient's confidentiality. In some instances,
the treatment options are provided over a secure website. The
patient is provided access to the secure website via a username and
password associated with the patient. In addition to providing the
patient information regarding the selected treatment option(s), the
patient interface also provides the patient with the ability to ask
questions. In some embodiments, the interface includes a query box
that is filled out and submitted by the patient, which a medical
professional replies to. In other embodiments, the interface is in
the form of a chat or instant messaging session. The patient may
ask questions over the chat session and the medical personnel can
provide answers to these questions immediately or seek answers to
the questions and reply to the patient at a later time. In yet
other embodiments, the patient interface may be combined with
video-conferencing or telephonic-conferencing to provide additional
information and opportunities for questions to the patient.
Referring to FIG. 12, shown therein is an exemplary screen shot 54
of a software interface that may be utilized as part of step 32. In
the current embodiment, a link 56 to a product information page
regarding an implant designed for the patient is provided on the
left hand side of the screen shot 54. In that regard, the product
information page designed for the patient includes generalized
information regarding the type of implant, the typical uses of the
implant, specifications of the implant, success stories related to
the implant, and/or other information related to the implant that
would be desirable to share with a patient. A link 58 to a product
information page regarding an implant designed for the physician or
medical personnel is provided on the right hand side of the screen
shot 54. The product information page designed for the physician
includes information related to the appropriate surgical approaches
available for inserting the implant, details of the preferred
surgical procedure(s), specifications of the implant, available
variations/models of the implant (e.g., sizes, materials, etc.),
and/or other information related to the implant that would be
desirable to share with the physician.
Referring to FIGS. 2 and 13, after selection of the treatment
option at step 32, the method 20 continues with step 34 in which
the execution of the selected treatment option is planned. In this
regard, a majority and/or all of step 34 is included in the
optimization of the treatment plans in step 30. However, not all of
the details of executing the treatment plan are necessarily
addressed in step 30. Further and as noted previously, in some
embodiments step 30 is not included and, therefore, planning the
execution of the treatment option is completed in step 34. In some
embodiments, planning the treatment option comprises planning the
surgical procedure utilized to insert the implant. For example,
referring more particularly to FIG. 13, shown therein is a flow
chart illustrating the planning step 34 of the method 20 according
to one embodiment of the present disclosure where the selected
treatment option is a surgical procedure. The planning step 34
begins with determining the desired placement and orientation of
the implant at step 124. The planning step 34 continues by
identifying any anatomical features that need to be preserved
through the surgical procedure at step 126. Further, the desired
fixation positions and orientations for any fixation devices are
established and marked on a model at step 128. These fixation
positions and orientations are saved for future reference during
the actual surgical procedure. With respect to the planned
placement and orientation of the implant and/or fixation devices,
an error field is established that identifies the expected range of
accuracy within which the implant and/or fixation devices should be
implanted. Based on this expected range of accuracy, a
corresponding expected range of performances is established for the
treatment plan. Image guided surgery techniques are utilized in
some embodiments to ensure that the treatment plan is executed
according to the desired positions and orientations. Further, the
position of the patient during each step of the treatment plan is
determined in some instances. For some treatment plans the patient
is moved between different positions for various steps of the
treatment plan. Accordingly, in some embodiments the selected
treatment option is planned in accordance with use of a dynamic
surgical table as described in U.S. Pat. No. 7,234,180, filed Dec.
10, 2004 and titled "Dynamic Surgical Table System," hereby
incorporated by reference in its entirety. Taking these various
factors into consideration planning the execution of the selected
treatment option is finalized at step 130.
Referring to FIGS. 2 and 14, after planning the execution of the
treatment plan at step 34, the method 20 continues with step 36 in
which the treatment plan is executed. Referring more particularly
to FIG. 14, shown therein is a flow chart illustrating the
performance step 36 of the method 20 according to one embodiment of
the present disclosure. At step 132, the treatment plan is executed
in accordance with the planning that has occurred in the previous
steps. In that regard, in the context of a surgical procedure the
procedure is monitored intra-operatively at step 134. The actual
surgical procedure, as monitored, is compared in real-time, or
approximately real-time, to the planned treatment at step 136.
Thus, the actual placement of the implant and fixation devices is
compared to the intended placement and/or associated error fields.
In this manner, an analysis of the placement of the surgical
components is performed before the patient leaves the operating
room. At step 138, the actual surgical procedure is modified as
needed to ensure that it coincides with the error fields of the
planned treatment. Thus, any initial adjustments that need to be
made can be accomplished without the need for a revision surgery or
a return to the operating room. In some embodiments, the position
of the implant and/or fixation devices is established using
implantable sensors located within the implant, fixation devices,
and/or insertion instruments. For example, in some instances the
implant and/or fixation devices include sensors such as those
described in U.S. patent application Ser. No. 10/985,108 filed Nov.
10, 2004; U.S. patent application Ser. No. 11/118,170 filed Apr.
29, 2005; U.S. patent application Ser. No. 11/344,667 filed Feb. 1,
2006; U.S. patent application Ser. No. 11/344,999 filed Feb. 1,
2006; U.S. patent application Ser. No. 11/356,687 filed Feb. 17,
2006; U.S. patent application Ser. No. 11/344,459 filed Jan. 31,
2006; U.S. patent application Ser. No. 11/344,668 filed Feb. 1,
2006; each of which is hereby incorporated by reference in its
entirety. In some embodiments, the surgery and/or other treatment
plans are performed using computer-guided surgical techniques that
are based on the selected treatment option.
Referring to FIGS. 2 and 15, after executing the treatment plan or
a part thereof at step 36, the method continues with step 38 in
which a post-treatment analysis is performed. In some embodiments,
the post-treatment analysis is substantially similar to steps 22,
24, and/or 26 described above. Referring more specifically to FIG.
15, shown therein is a flow chart illustrating the post-treatment
analysis step 38 of the method 20 according to one embodiment of
the present disclosure. In that regard, the post-treatment analysis
step 38 includes comparing the predicted results of the modeling of
step 30 to the actual results of the treatment at step 140. Any
discrepancies between the model and the actual results are
identified at step 142. At step 144, the discrepancies are utilized
to improve the correlation between the model and actual results. In
that regard, the parameters utilized for creating the models are
updated and modified based on the identified discrepancies.
Ideally, the predicted results provided by the model are
substantially similar to the actual results of the treatment plan.
In some embodiments, the post-treatment analysis is performed at
set intervals after the surgical procedure. In one particular
embodiment, the patient goes through post-treatment analysis at
least at 2 weeks, 6 weeks, and 3 months after the surgical
procedure. In some embodiments, sensors located within the implant
and/or fixation devices are utilized in the post-treatment analysis
to obtain data related to the patient's motion sequence(s).
By monitoring the resultant data from each patient for each
treatment plan, a statistical correlation between medical
conditions and treatment options is established. This statistical
correlation is utilized in selecting the treatment plans for
subsequent patients. For example, in some instances the method
includes step 39 that comprises a feedback loop to an earlier step
in the method, such as step 30 for example. In that regard,
modeling of the treatment options at step 30 can be updated to
correspond with the outcomes as observed in the post-treatment
analysis of step 38. In that regard, in some instances the current
patient's resultant data is routed and stored as a part of a study
and/or other collection of data into a database for future access
by the system 10. Generally, the data will need to be de-identified
from the particular patient, so as to preserve confidentiality and
impartially of the data and to comply with applicable laws. For
example, the patient's name, social security number, address,
and/or other sensitive information are removed from the data, while
the patient's physical characteristics, selected treatment plan,
and outcome are maintained. In some embodiments, the data is
entered into the databases by a medical professional as part of the
post-treatment analysis of step 38 of the method 20. This data
related to current patient's outcome is the feedback that provides
confirmation of prior information and/or new information from which
the medical professionals can modify the treatment plans and/or
medical device manufacturers can modify the implants or
devices.
Further, in some instances the database includes information
regarding whether the patient's treatment plan was an on-label or
off-label use of a medical product. In that regard, in some
embodiments the database and/or software interface includes a field
that allows the treating physician or medical personnel to describe
the particular use of the medical product. Accordingly, a later
physician can evaluate the possibility of such a use for his or her
patient. The database or system can highlight off-label uses so
that treatment plans for later patients are not adversely affected
by previous off-label uses that skew the data results. In some
embodiments, the database includes information regarding
reimbursement procedures. In that regard, the database includes the
various requirements for obtaining reimbursement from various
insurance companies in some embodiments. Further, the database
keeps track of the success of previous reimbursement requests based
on the associated patient data in some instances. Accordingly, a
treating physician is able to evaluate the likelihood of being
reimbursed from a particular insurance company for a selected
treatment plan.
Referring again to FIGS. 1 and 2, the system 10 and, in particular,
the modules 12, 14, 16, and 18 may provide a platform for executing
some or all of the steps of the method 20 described above.
Accordingly, aspects of the system 10 will now be described in
connection with the method 20. The diagnosis module 12 is adapted
to execute some or all portions of steps 22, 24, and 38. In that
regard, the diagnosis module 12 prompts a user to answer a series
of diagnostic questions and/or provide one or more menus for
selecting items indicative of the patient's medical condition. The
exemplary screen shot 40 of the software interface shown in FIG. 3
is utilized in some embodiments. The diagnosis module 12 is also
configured to process patient diagnosis data in addition to, or in
lieu of, the diagnostic questions. In some embodiments, imaging
techniques are utilized to evaluate the patient and the diagnosis
module may be adapted to receive, store, and/or process the images.
For example, in some embodiments radiographic images of the
patient's anatomy are obtained, transferred to the diagnosis module
12, and stored in a database accessible by the diagnosis module 12.
The radiographic images are then analyzed by the diagnosis module
12 and/or the physician to identify any medical conditions
afflicting the patient. In some embodiments, the patient is put
through a series of movements appropriate to determine the
patient's motion sequence and/or range of motion for one or more
anatomical areas. The patient's motion sequence and/or range of
motion in each area are captured, transferred to the diagnosis
module 12, and utilized by the diagnosis module in evaluating the
patient. The diagnosis module 12 is configured to receive other
data sets or information and take such data into account during the
evaluation of the patient in some embodiments.
For example, the diagnosis module 12 is adapted to receive patient
data related to an imaging study in some embodiments. The imaging
study includes patient images obtained through the use of magnetic
resonance imaging ("MRI"), computed tomography ("CT"), video
fluoroscopy, and/or other imaging techniques. In some embodiments,
the imaging study includes techniques as described in commonly
owned U.S. patent application Ser. No. 11/697,426 filed Apr. 6,
2007 and titled "System and Method for Patient Balance and Position
Analysis", herein incorporated by reference in its entirety. In
some embodiments, the imaging study of step 24 includes tracking
the movement of anatomical features of the patient using sensors.
The imaging data is stored in a database accessible by the
diagnosis module 12.
Based on the response to the diagnostic questions and/or other
types of patient analysis data obtained, the diagnosis module 12
groups the patient into a particular classification of patient. In
some embodiments, the classification is by type of injury or
medical condition. In other embodiments, the classification is
based on other patient factors such as height, weight, age, or
otherwise. In some embodiments, the classification is at least
partially based on a treating physician or other medical
personnel's preferences that are selected or otherwise defined
within the diagnosis module 12. It is contemplated that in some
instances each classification is further subdivided into groups
based on factors such as the severity of the condition, age,
health, and/or other factors. In some embodiments, the
classifications and groupings are based on factors identified in
clinical studies and/or past patient treatments as being indicators
of success for the available treatment options. In that regard, the
diagnosis module 12 is in communication with a database containing
information regarding past clinical studies and/or patient
treatments that may be utilized in diagnosing the current
patient.
The modeling module 14 is adapted to execute some or all portions
of steps 26, 30, 32, 34, and 36 of the method 20. In that regard,
the modeling module 14 synthesizes the information obtained by the
diagnosis module 12 during steps 22 and 24 to identify the abnormal
medical conditions afflicting the patient. In that regard, the
modeling module 14 creates a 3-D and/or 2-D animated model of the
patient's anatomy. The animated model is based substantially on the
imaging data obtained by the diagnosis module 12. In some
embodiments, the modeling module 14 is used to highlight the
problem areas and/or times in the patient's anatomical motion
sequence. In that regard, the modeling module 14 allows selection
of particular layers of anatomical features. For example, in one
embodiment the patient's motion anatomy is grouped into layers
according to types of anatomical tissue, such as bones, cartilage,
ligaments, tendons, muscles, and/or combinations thereof. The
modeling module 14 provides a user interface allowing medical
personnel to select the layers of anatomical tissue to be
considered in modeling the patient's motion. The modeling module 14
analyzes the motion according to the selected grouping of
anatomical tissue and the interactions therebetween.
In some embodiments, the modeling module 14 is configured to
combine diagnostic tests with the imaging study in creating the
animated model. For example, in some embodiments the modeling
module 14 combines muscle monitoring with the imaging study to
identify muscle contractions and tensions during a particular
motion sequence or protocol. In other embodiments, the modeling
module 14 utilizes center-of-balance and/or center-of-gravity data
for the patient obtained by the diagnosis module 12. In some
embodiments, devices and methods as described in commonly owned
U.S. Pat. No. 7,361,150 filed Jun. 25, 2004 and titled "Method and
Device for Evaluating the Balance Forces of the Skeleton," herein
incorporated by reference in its entirety, are utilized. Muscle
monitoring and center-of-balance data are merely examples of the
types of additional data that are used with the imaging data by the
modeling module 14 in forming the animated model. In other
embodiments, the modeling module 14 is adapted to utilize other
types of the patient data as well.
The modeling module 14 includes additional features to allow
medical personnel and/or a computer system to analyze the patient.
In that regard, in some embodiments the modeling module 14 creates
a stress grid overlay that highlights potential areas of increased
stress or strain on the patient's anatomy, such increased muscle
activity; overstretching of muscles, ligaments, and/or tendons;
friction between bones; and/or other areas of stress/strain. In
some embodiments, the module 14 provides a user interface that
allows for zooming, panning, or otherwise changing the orientation
of the view of the patient's anatomy. A user adjusts the
orientation to better observe or isolate a potential problem area.
Similarly, in some embodiments the module 14 provides a user
interface that allows a user to pause, rewind, slow down, and/or
speed up simulation of a motion sequence to better observe a
potential problem. Further, the modeling module 14 allows 3-D
and/or 2D tracking of specific anatomical features through the
motion sequences in some instances. In some embodiments, the
modeling module 14 highlights potential problem areas for the
patient based on a comparison to a standardized model associated
with the patient. In that regard, the modeling module 14 is in
communication with a database containing a plurality of
standardized models for such use. In some embodiments, the problem
areas are identified by the modeling module 14 by identifying an
abnormal motion pattern.
The modeling module 14 is also utilized in modeling the selected
treatment options. Modeling of the treatment options builds upon
the animated model of the patient used during the patient diagnosis
and analysis. Thus, in many aspects the module 14 utilizes the same
features described above in modeling the treatment options.
However, in modeling the treatment options the modeling module 14
modifies the model by replacing a damaged portion of the patient's
anatomy with an implant. The module 14 then utilizes the
characteristics of the implant in modeling the patient's anatomical
motion sequences. Further, in some embodiments the modeling module
14 further modifies the model by taking into consideration the
surgical approach that will be used and any corresponding anatomy
that will be sacrificed by the surgical approach. In this manner,
the modeling module 14 provides an estimation of the outcome of the
treatment plan taking into account these additional factors. In
that regard, the treating physician may optimize each treatment
plan by utilizing the modeling module 14 to modify such factors as
the size, placement, orientation, and material properties of a
particular implant and/or modifying the surgical procedure to
adjust the tissues that will be compromised.
The modeling module 14 is also utilized in planning the selected
treatment option in some instances. In some embodiments, the
planning includes determining an optimized surgical procedure for
inserting the implant. In that regard, the modeling module 14 takes
into account such factors as the desired placement and orientation
of the implant and/or the need to preserve certain anatomical
features in determining the appropriate surgical procedure.
Further, in some embodiments desired fixation positions and
orientations for the fixation devices are established and marked on
the model created by the modeling module 14. These fixation
positions are saved for future reference during the actual surgical
procedure. With respect to the planned placement and orientation of
the implant and/or fixation devices, the modeling module 14
establishes an error field that identifies the expected range of
accuracy in which the implant and/or fixation devices will be
implanted. Based on this expected range of accuracy, a
corresponding range of performances are established for the
treatment plan and modeled by the modeling module 14. For each of
the selected treatment plans and/or implants, the treating
physician may save one or more optimized plans in a database or
other memory accessible by the modeling module 14.
Subsequently, the optimized plans of the modeling module 14 are
utilized in the execution of the treatment plans. For example, the
optimized plans of the modeling module 14 are used during a
surgical procedure to guide the physician to the appropriate
placement of an implant and/or fixation device. The surgical
procedure is monitored intra-operatively and compared in real-time,
or approximately thereto, to the planned treatment. Thus, the
actual placement of the implant and fixation devices is compared to
the intended placement and/or the associated error fields. In some
embodiments, the position of the implant and/or fixation devices is
established using implantable sensors located within the implant,
fixation devices, and/or insertion instruments. In some
embodiments, the surgery or other treatment plan is performed using
computer-guided surgical techniques that are based on the optimized
treatment option created with the modeling module 14.
The treatment module 16 is adapted to execute some or all portions
of steps 28, 30, 32, 34, and 36 of the method 20. In that regard,
the treatment module 16 identifies the available treatment options
for a particular patient. The treatment module 16 will identify the
available treatment options based upon the patient analysis
performed by the diagnosis module 12 and the modeling module 14.
Thus, in some embodiments the treatment options may be determined
by looking at the results of the patient analysis, identifying the
patient's medical condition(s), and proposing treatment plans based
on the patient's medical condition(s). The treatment module 16 may
propose treatment plans that include surgical procedures,
non-invasive treatments, and/or medicinal treatments. In some
embodiments, the treatment module 16 facilitates sorting and/or
screening of the treatment options. In some embodiments, the
treatment options are sorted and/or screened based on physician
preference. For example, if a physician prefers surgical procedures
that utilize a posterior approach, then the available treatment
options are limited to those implants and surgical procedures that
may be implanted using a posterior approach. The treatment module
16 also provides a user interface for selecting the physician's
preferences. As another example, in some instances the treatment
options are sorted based on the success of the treatment plan for
previous patients having a similar profile to the current patient.
Similarly, the treatment options are sorted based on the previous
procedures performed by the treating physician/surgeon and the
relative success of those procedures in some instances. In each of
these examples, the treatment module 16 is in communication with a
database containing the relevant information for sorting and/or
screening the treatment options. For example, in at least one
embodiment the physician's preferences are stored in a database
that is accessible by the treatment module 16 when the physician
logs into the system using a username and password. Similarly, a
database maintaining the results of the previous treatment options
and the patient details for these treatment options is accessible
by the treatment module 16 in some embodiments.
The post-treatment feedback module 18 is adapted to execute some or
all portions of step 38 of the method 20. In some aspects the
post-treatment feedback module 18 is substantially similar to the
diagnosis module 12. In that regard, in some embodiments the system
10 does not include a separate post-feedback module 18. Rather, the
diagnosis module 12 and the post-feedback module comprises a single
module. The post-treatment feedback module 18 is utilized to
compare the predicted results of the modeling module 14 to the
actual results of the treatment plan. Any discrepancies between the
model and the actual results are identified by the post-treatment
feedback module 18 and utilized to improve the correlation between
the model and actual results. Ideally, the predicted results
provided by the model are substantially similar to the actual
results of the treatment plan. In some embodiments, sensors located
within the implant and/or fixation devices are utilized by the
post-treatment feedback module 18 to obtain data related to the
patient's motion sequence(s). By monitoring the resultant data from
each patient for each treatment plan, a statistical correlation
between medical conditions and treatment options is established.
This statistical correlation and/or underlying data are stored in a
database accessible by the diagnosis module 12, modeling module 14,
and/or the treatment module 16 and are utilized in conjunction with
subsequent patients for diagnosing, modeling, and/or selecting
appropriate treatment plans.
It is understood that while each of the modules 12, 14, 16, and 18
have been described as having particular functions no limitations
are intended thereby. In that regard, the functions described above
with respect to a particular module may be performed by other
modules and/or multiple modules. In some embodiments the functions
of two or more of the modules may be performed by a single module.
In other embodiments, the function(s) of a single module may be
distributed across multiple modules. It is understood that the term
module may include software, hardware, and/or combinations of
hardware and software.
Referring now to FIG. 16, shown therein is an illustrative node 60
for implementing embodiments of the systems and methods described
above. Node 60 includes a microprocessor 62, an input device 64, a
storage device 66, a video controller 68, a system memory 70, and a
display 74, and a communication device 76 all interconnected by one
or more buses 72. The storage device 66 could be a floppy drive,
hard drive, CD-ROM, optical drive, or any other form of storage
device. In addition, the storage device 66 may be capable of
receiving a floppy disk, CD-ROM, DVD-ROM, or any other form of
computer-readable medium that may contain computer-executable
instructions. Further communication device 76 could be a modem,
network card, or any other device to enable the node to communicate
with other nodes. It is understood that any node could represent a
plurality of interconnected (whether by intranet or Internet)
computer systems, including without limitation, personal computers,
mainframes, PDAs, and cell phones.
A computer system typically includes at least hardware capable of
executing machine readable instructions, as well as the software
for executing acts (typically machine-readable instructions) that
produce a desired result. In addition, a computer system may
include hybrids of hardware and software, as well as computer
sub-systems.
Hardware generally includes at least processor-capable platforms,
such as client-machines (also known as personal computers or
servers), and hand-held processing devices (such as smart phones,
personal digital assistants (PDAs), or personal computing devices
(PCDs), for example). Further, hardware may include any physical
device that is capable of storing machine-readable instructions,
such as memory or other data storage devices. Other forms of
hardware include hardware sub-systems, including transfer devices
such as modems, modem cards, ports, and port cards, for
example.
Software includes any machine code stored in any memory medium,
such as RAM or ROM, and machine code stored on other devices (such
as floppy disks, flash memory, or a CD ROM, for example). Software
may include source or object code, for example. In addition,
software encompasses any set of instructions capable of being
executed in a client machine or server.
Combinations of software and hardware could also be used for
providing enhanced functionality and performance for certain
embodiments of the present disclosure. One example is to directly
manufacture software functions into a silicon chip. Accordingly, it
should be understood that combinations of hardware and software are
also included within the definition of a computer system and are
thus envisioned by the present disclosure as possible equivalent
structures and equivalent methods.
Computer-readable mediums include passive data storage, such as a
random access memory (RAM) as well as semi-permanent data storage
such as a compact disk read only memory (CD-ROM). In addition, an
embodiment of the present disclosure may be embodied in the RAM of
a computer to transform a standard computer into a new specific
computing machine.
Data structures are defined organizations of data that may enable
an embodiment of the present disclosure. For example, a data
structure may provide an organization of data, or an organization
of executable code. Data signals could be carried across
transmission mediums and store and transport various data
structures, and, thus, may be used to transport an embodiment of
the present disclosure.
The system may be designed to work on any specific architecture.
For example, the system may be executed on a single computer, local
area networks, client-server networks, wide area networks,
internets, hand-held and other portable and wireless devices and
networks. In that regard, it is understood that the network may be
a secure network to comply with patient confidentiality
requirements and otherwise protect patient data and/or proprietary
information.
A database may be any standard or proprietary database software,
such as Oracle, Microsoft Access, SyBase, or DBase II, for example.
The database may have fields, records, data, and other database
elements that may be associated through database specific software.
Additionally, data may be mapped. Mapping is the process of
associating one data entry with another data entry. For example,
the data contained in the location of a character file can be
mapped to a field in a second table. The physical location of the
database is not limiting, and the database may be distributed. For
example, the database may exist remotely from the server, and run
on a separate platform. Further, the database may be accessible
across the Internet. Note that more than one database may be
implemented.
When a surgeon has performed an orthopedic spinal treatment, the
data from that treatment is commonly retained by the surgeon and,
in many cases, is submitted to studies of various types for
assimilation. The data may be gathered and stored in a database
accessible by the modules of the system 10 for future access and
analysis. The data may be sorted and organized in various
hierarchies. For example, a first level may include generally all
received data without limiting the outcomes or patient pathology by
type of outcome, anatomical area of treatment, or other specific
category. A second level, for example, may filter the general data
down to data or studies obtained by various study groups. For
example, a spine trauma study group (STSG) may focus on injury to
spinal region such as the vertebrae and associated tissue. A third
level, may filter data within each of the various study groups. For
example, continuing the STSG example, the spinal data may be
filtered down to data or studies to a particular region of the
spine, such as a lumbar spine study group (LSSG), which may focus
on treatments and outcomes in solely the lower back (e.g. lumbar
and sacral vertebrae and associated tissue). Similarly, another
grouping may filter the spinal data down to data or studies by a
cervical spine study group (CSSG), which may concentrate on
outcomes and treatments in the upper vertebral region, including
the neck and occiput. Yet another grouping may filter the spinal
data down to data or studies by a spinal deformity study group
(SDSG), which may study and report data concerning treatment of
scoliosis and other deformative conditions. Numerous other
groupings and divisions may be created to further subdivide the
data and studies of the various study groups. The data collected by
the surgeon or his or her team at each level may include a variety
of numerical, language, image (e.g. radiographic) or other data,
and can include data sufficient to perform the related surgical
operations. Instructions or training as to the data to be provided
by the surgeon or his or her team may be provided by a user
interface and/or personal training.
The data from these studies can be provided to one or more software
applications in order to derive further information or new views of
the data. Examples of such software include the Montreal 3D
Radiographic Modeling, Measurement and Surgery Simulation software
("Montreal software"); the TruBalance patient measurement software
("TruBalance software"); and the DRPro radiographic measurement
software offered by PhDx eSystems, Inc. of Albuquerque. Other
brands or types of software for obtaining, analyzing, or otherwise
handling patient data may be used in addition to or instead of one
or more of the software applications mentioned above for one or
more of the data categories. Also, multiple software applications
may be applied to a given set of data. It is understood that data
from each study can be assembled together prior to submission to
such software, or each study can be treated individually.
Output from the software applications may be routed to one or more
databases for storage. These databases may be physically distant
from each other, but may be connected via electronic connections,
such as hard-wired connections, internet or other network
connections, and/or satellite connections. In that regard, in some
embodiments the databases may be directly accessible by the modules
12, 14, 16, and 18 of the system 10 for use in the patient
diagnosis, analysis, and treatment planning. In other instances,
data from these databases and other sources may be compiled to
create a master database. It is understood the data need not
necessarily reside on a single server or hard-drive system to form
the master database. Rather, the databases containing data from
each of the studies may feed data into or be accessible via the
master database. Further, it is understood that data may be passed
to other databases or outputs. Data or other outputs from the
database(s) may be output in the form of a report. The report may
be in a computer readable form and/or in human intelligible form.
In that regard, the report may be utilized by the system 10 and/or
a treating physician in determining appropriate treatment options
for a patient.
Medical professionals, for example spinal surgeons, may access the
database(s) via a software interface or computer network. This
assessment, treatment and outcomes modeling software of the system
10 allows entry of data of a current patient for which a diagnosis
and/or treatment options and analysis is desired. That data may
then be compared to the data available in or through the accessible
database(s). If necessary, the data obtained from or through
database(s) may be buffered or otherwise copied and transformed,
e.g. via mathematical or other algorithm, so that it can be more
efficiently compared to the current patient data via human or
machine review. The comparisons and/or transformations may be made
using the IRT models or equations, in certain embodiments. Such a
comparison can be used to obtain records of previous medical cases
in which patients having similar characteristics (e.g. gender,
height, weight, age or affliction) were treated with various
treatments, and their corresponding outcomes. In this way, the
medical professional can quickly obtain a view of outcomes of one
or more treatments for his or her current patient, and/or the
likelihood of a positive outcome for a given treatment. With this
information and his or her personal knowledge and experience, the
professional can come to a treatment decision or recommendation
more quickly, more efficiently, and with greater likelihood of
successful treatment outcome.
Other uses can also be made of the outputs of the database(s). In
addition to medical professionals studying previous outcomes for
guidance on a current patient's case, bioengineers can study
outcomes having to do with particular products or afflictions
toward improving existing implants or other devices and/or creating
new devices and treatment plans. Further, as a repository of
clinical and treatment information, the database(s) may also be
used by historians, epidemiologists, or others with an interest in
such data.
In addition to data communication between medical personnel
(physicians, surgeons, medical assistants, etc.), patients, and/or
medical device manufacturers, the system 10 may also provide a
communication link to a treatment facility. In one embodiment, the
system 10 is in communication with a physical therapy treatment
facility such that the treating physical therapist may. In other
embodiments, the system 10 may be in communication with other
treatment facilities or rehab centers. As post-operative therapies
can play a significant role in the overall effectiveness of a
surgical procedure, in some embodiments post-operative therapies
may be a component of the available treatment plans for the
patient. In other embodiments, the system 10 may include a separate
module for determination of appropriate post-operative therapies
based on the treatment plan selected for the patient and/or other
factors.
Among other things, there is disclosed a system for use by medical
professionals and other experts for assisting them in making
decisions on treatments aimed at the desired clinical outcome for
the patient. Such systems may include methods for coordinating a
number of sources of data and assimilating relevant data to provide
actionable information for the professional. Using such information
with the appropriate confidence intervals, a surgeon (for example)
can come to a clinical decision on treatment based on data of
actual patient outcomes relating to identical or similar
treatments. In particular embodiments, the data analysis can take
the form of an item response theory (IRT) modelization technique
that allows a professional to compare his or her patient's data to
actual previous patient outcomes data that have been conditioned
with a probabilistic treatment result. The system may perform in a
methodical and predictable way one or more of the variable or
"trade-off" decisions among possible treatment options normally
made by skilled professionals.
The methods and systems disclosed herein were originally developed
for use with orthopedic surgery cases, and in particular with
orthopedic treatments of the spine. Accordingly, the following
description will use the context of spinal orthopedic medicine and
treatments used therein. It is to be understood that identical or
similar methods or systems could be used in other medical fields,
such as cardiology or oncology.
Referring generally to FIG. 17, shown therein is a flow chart of an
embodiment of a data flow method according to another aspect of the
present disclosure. As indicated, this embodiment reflects storage,
movement and usage of data obtained from treatment of patients.
Block 220 reflects that treatment, which in the spinal orthopedic
field may include open or minimally-invasive surgery, stabilization
through implantation of rods, plates and/or disc prostheses, fusion
of one or more vertebral levels via intervertebral cages, placement
of osteogenic materials, or many other procedures. The decision as
to what treatment to use may come from a surgeon's fundamental
knowledge of biology, particularly anatomy, and current and past
basic research in the field. The decision may also be influenced
via reports or other information of results from other surgeries or
treatments, as indicated at blocks 222 and 224, and further
discussed below.
When a surgeon has performed an orthopedic spinal treatment, the
data from that treatment is commonly retained by the surgeon and,
in many cases, is submitted to studies of various types for
assimilation. Blocks 226, 228, 230, 232, and 234 indicate such
gathering by entities into a repository of outcomes for access and
analysis. Block 226 reflects a gathering of outcomes generally,
without necessarily limiting outcomes or patient pathology by type
of outcome, anatomical area of treatment, or other specific
category. Block 228 reflects studies by a study group A. In one
exemplary embodiment, study group A is a spine trauma study group
(STSG) that focuses on injuries to vertebrae and associated tissue.
Block 230 reflects studies by a study group B. In one exemplary
embodiment, study group B is a lumbar spine study group (LSSG),
which focuses on treatments and/or outcomes in the lower back,
(e.g. lumbar and sacral vertebrae and associated tissue). Block 232
reflects a collection of data by a study group C. In one exemplary
embodiment, study group C is a cervical spine study group (CSSG),
which concentrates on outcomes and treatments in the upper
vertebral region, including the neck and occiput. Block 234 shows
data collection by a study group D, which in one exemplary
embodiment is a spinal deformity study group (SDSG) that focuses on
the treatment of scoliosis and other deformative conditions. The
data collected by the study groups A, B, C, and D can include a
variety of numerical, language, image (e.g. radiographic) or other
data, and can include data sufficient to perform the operations
noted below. Instructions or training as to the data to be
collected by a surgeon or his or her team working within each of
the study groups A, B, C, and D is provided in some instances to
ensure that all or at least a majority of the pertinent information
is collected.
The data from these studies can be provided to one or more software
applications in order to derive further information or new views of
the data. Examples of such software are indicated in blocks, 236,
238, and 240. Block 236 shows a 3D modeling, measurement, and
simulation software. In some instances, the 3D modeling,
measurement, and simulation software is the Montreal 3D
Radiographic Modeling, Measurement, and Surgery Simulation software
("Montreal software"). The Montreal software takes provided data
and provides outputs with radiographic models or other images,
provides measurements relevant to the anatomy and procedure, and
can create a simulation of surgical procedure(s). Block 238 shows a
patient measurement software. In some instances, the patient
measurement software is configured to obtain patient balance
information, including center-of-balance information. In some
embodiments, TruBalance patient measurement software ("TruBalance
software") is utilized. Block 140 shows a radiographic measurement
software that is utilized to obtain measurements of the patient's
anatomical features from a radiographic image. In some instances,
DRPro radiographic measurement software offered by PhDx eSystems,
Inc. of Albuquerque is utilized. The DRPro software can be used to
measure and otherwise obtain data from radiographs or other images.
Other brands or types of software for obtaining, analyzing, or
otherwise handling patient data could be used in addition to or
instead of one or more of the software applications described above
one or more data categories. Also, multiple software applications
may be applied to a given set of data. In the embodiment of FIG.
17, as one example, output or data from any of the study groups of
blocks 226, 228, 230, 232, and 234 can be routed for handling by
the radiographic measurement software of block 240. It will be seen
that data from each study can be assembled together prior to
submission to such software, or each study can be treated
individually.
As seen in embodiment of FIG. 17, output from blocks 236, 238, and
240 may be routed to databases for storage. Block 242 indicates a
3-D Modeling, Measurement, and Simulation Study Data and Image
Store that includes output of models, measurements, simulations and
other images or information from the 3-D modeling, measurement, and
simulation software (block 236). Block 244 indicates a Patient
Measurement Study Data and Image Store that includes output of the
information from the patient measurement software (block 238).
Block 246 indicated a Radiographic Measurement Study Data and Image
Store that includes radiograph information, study data, and/or
other information from the radiographic measurement software (block
240) and/or from studies, such as those indicated in blocks 226,
228, 230, 232, and 234. These databases may be physically distant
from each other, but may be connected via electronic connections,
such as hard-wired connections, internet or other network
connections (wired or wireless), and/or satellite connections.
Data from these databases and other sources can be brought together
to a master database, shown in block 250 and labeled "Spine
Registry Data Mart." Block 250 brings together data from blocks
242, 244, and 246. The data from the blocks 242, 244, and 246 is
aggregated into the database of block 250 in some instances. In
other instances, the data from blocks 242, 244, and 246 is
accessible from the database of block 250, but not necessarily part
of the database of block 250. In the illustrated embodiment, one or
more independent databases shown in block 251 are included in or
accessible by the master database of block 250. As shown the
independent databases 251 are available to the master database via
one or more Independent Study Data Stores, shown in block 252. It
will be seen that other databases, e.g. databases dedicated to data
from other studies noted above, could also feed data into or be
accessible via the database in block 250. In some instances, the
independent databases include the Scolisoft database or the Spine
Tango database. Further, it will also be seen that data can be
passed to other databases or outputs. As shown in FIG. 17, data or
other output from block 246 may be output in the form of reports,
shown generally at block 224. As indicated above, physicians in the
course of considering or giving treatment (block 220) may consult
such reports.
Medical professionals, for example spinal surgeons, can access this
database in block 250 via the software in block 254, labeled "Spine
ATOM." This assessment, treatment and outcomes modeling software
allows entry of data of a current patient for which a diagnosis
and/or treatment options and analysis is desired. That data is then
compared to the data available in or through database 250. If
necessary, the data obtained from or through database 250 may be
buffered or otherwise copied and transformed, e.g. via mathematical
or other algorithm, so that it can be more efficiently compared to
the current patient data via human or machine review. The
comparisons and/or transformations may be made using the IRT models
or equations, in certain embodiments. Such a comparison can be used
to obtain records of previous medical cases in which patients
having similar characteristics (e.g. gender, height, weight, age or
affliction) were treated with various treatments, and their
outcomes. In this way, the medical professional can quickly obtain
a view of outcomes of one or more treatments for his or her current
patient, and/or the likelihood of a positive outcome for a given
treatment (block 256). With this information and his or her
personal knowledge and experience, the professional can come to a
treatment decision or recommendation more quickly, more
efficiently, and with greater likelihood of successful treatment
outcome. As indicated in FIG. 17, the study of outcomes (block 256)
can translate into reports of other information (block 222) that
assist the physician in considering options or planning
treatment.
Other uses can also be made of the output of the ATOM analysis. In
addition to medical professionals studying previous outcomes for
guidance on a current patient's case, bioengineers can study
outcomes having to do with particular products or afflictions
toward improving existing implants or other devices or creating new
devices and treatments (block 258). Blocks 260 and 262 represent
uses by management to review outcomes for market, treatment and
other trends. The data based on blocks 258, 260, and 262 and/or the
decisions resulting therefrom are utilized in developing new
products and modifying existing products in the product pipeline
shown in block 264. As a repository of clinical and treatment
information, a database such as that shown in block 250 could also
be used by historians, epidemiologists, or others with an interest
in such data.
Referring now generally to FIG. 18, shown therein is an embodiment
of a patient diagnostic model according to another aspect of the
present disclosure. The model begins generally when medical
professional(s) consult with a patient (block 266), either as an
initial appointment or through a referral. Again using the context
of spinal surgery solely for illustration, at or after such
consultation both the patient (block 268) and the professional(s)
(block 270) provide information on study forms or in other ways.
Such information may include standard information on the physical
characteristics and condition of the patient. In a particular
embodiment, such information may also include a series of options
for the patient and/or the medical professional to select. Such
options are geared toward helping to determine and appropriate
treatment, as disclosed herein, and could include goals as to
post-operative mobility, activity or relative deformity,
pre-operative condition, prior surgeries or other treatments, or
other factors. The information may also include weighting or come
or all factors so as to emphasize one or more factors as the
analysis of potential treatments is performed.
In addition, radiographs (e.g. x-rays, MRI images, CT scans) or
other images can be taken of the current patient (block 272). Data
from these images are taken via software, and in the illustrated
embodiment 3-D modeling, measurement, and simulation software is
used to generate a three-dimensional model of the patient's spine
(block 274), patient measurement software is used to calculate a
global balance (block 276), and radiographic measurement software
is used to measure the images (block 278). An additional step that
can be used is to measure the radiographs and/or the 3-D images
generated by the software at block 274 with additional measurement
software (block 280). In some instances, software known as
Clindexia is utilized at block 280. Generally, these software
applications of blocks 274, 276, 278, and 280 transform the raw
images into mathematical or other forms that can easily be compared
via a computer or similar machine to images or other data from a
database (e.g. database 250 of FIG. 17) that have been similarly
transformed.
The information from the radiographs or other images and the
information from the patient's and medical professional's study
forms are combined into a file or database (block 282) in the
illustrated embodiment. That patient's clinical assessment data
then be used to help the medical professional(s) to select an
appropriate treatment, as described above. For example, some or all
of the data can be stored in a local file, disc or server (block
284), so that it can be accessed easily by a computer or other
processor that is also able to access the information available in
or through database 250. Block 286 reflects the analytical process.
Two subprocesses are shown in block 286, the first of which is the
ATOM process of comparing the current patient's data to aggregate
data of other patients and their treatments and outcomes. The
second subprocess shown in block 286 is a simulation of surgery
based on the current patient's data, including weighted factors
concerning the patient's characteristics or desired outcome. This
surgical simulation is performed with a software application in
some instances. In one particular embodiment, software known as S3
Spine Surgery Simulator is utilized. Using these subprocesses, a
professional can select a possible treatment based on the
comparison of his or her patient's characteristics and weighted
factors to previous patients, treatments and outcomes, and simulate
that treatment to calculate the likelihood of a successful outcome
(as suggested by the data collected in blocks 270 and 272).
One or both subprocesses shown in block 286 may be used, and either
or both may be used multiple times as the surgeon or other medical
professional may desire, so that the professional can evaluate as
many treatment scenarios as he or she deems appropriate. Once the
subprocesses have been run, the professional can select a treatment
(block 288) that best meets the patient's characteristics,
affliction and stated goals or weighted outcome factors, and
appears to be most likely to achieve the desired outcome. That
selection is used in choosing implants, devices, compositions, and
other products for the treatment (block 290), in preparing for an
implementing the treatment and obtaining an outcome (block 292),
and in developing data from the treatment and the outcome (block
294).
Block 296 in the embodiment of FIG. 18 indicates the routing and
storage of the current patient's data as a part of studies and/or
other collection of data into databases such as those noted above
with respect to FIG. 17. Once that data is appropriately
de-identified with the particular patient, so as to preserve
confidentiality and impartially of the data, and to comply with
applicable laws, it can optionally be entered into such databases,
e.g. following block 282. As discussed above, data from those
databases is used in at least the comparison(s) performed at block
286. Additionally, the data obtained in block 294 of the outcome of
the current patient's procedure can be transferred to such
databases, again after being appropriately de-identified. This new
data is the feedback to the databases that provides confirmation of
prior information and/or new information from which professionals
can learn in the future.
FIG. 19 shows schematically an embodiment of a system that is used
to perform the methods disclosed herein according to one aspect of
the present disclosure. A processor or central processing unit 297
is shown electronically linked to one or more databases 298 and to
one or more input/output devices 299. More than one processor may
be used, in the form of one or more computers or other devices, or
a single processor may be programmed to accomplish tasks discussed
herein. Processor 297 may be a part of a network, such as the
internet. Databases such as those described above may be
individually linked to processor 297, as the line between blocks
297 and 298 suggests, or may be physically or electronically
combined so that a single electronic link exists between processor
297 and database(s) 298. Input/output devices 299 may be physically
proximate or remote items such as disc drives, monitors, printers,
or other devices for inputting and outputting information from
processor(s) 297. Thus, current patient information may be inputted
via input/output device(s) 299 to processor(s) 297, which can
compare that information (as discussed herein) to data from
database(s) 298. An output of the comparison(s) may be received via
input/output device(s) 299. Processor(s) 297 may also be programmed
to perform treatment simulations (as discussed herein), again with
output being received via input/output device(s) 299.
The methods described above can be performed in any of a variety of
ways. In one embodiment, the data are transferred to electronic
media, is they are not taken or recorded immediately in that form,
and are similarly stored in such media. The various databases and
software discussed above may be available at a single geographic
location, or may be linked together electronically or simply
accessible by appropriate electronic devices. As one example, the
databases may be accessible to a particular computer via a network,
such as intranet, a dedicated network, or the internet, and the
particular computer may have the software necessary to access the
data and make the comparisons and analyses noted above.
With the above described embodiments, an algorithm, or expert
system to provide medical personnel involved with patient care a
modelization technique for optimizing the treatment of the patient.
Such optimization is created through assessment factors (e.g.
characteristics of the patient and desired goals), treatment
factors (e.g. efficacy or invasiveness), and outcome factors (e.g.
desired post-operative condition) relative to a pathology of a
patient's condition. Such a treatment algorithm can be derived from
a compilation of aggregated study data to which weighted factors
selected by medical professionals are applied. The weighted factors
are provided as options in answering questions in one or more study
questionnaires. From those weighted factors and the aggregated
data, it is contemplated that medical professionals may identify a
representative or simulated patient from within the aggregated data
set. Once that particular patient or data simulating a particular
patient is found, the professional may model a variety of clinical
outcomes parameters based on a set of initial conditions and
proposed treatment alternatives. Each proposed treatment
alternative will provide a likely outcome or a range of likely
outcomes, and the medical professional can evaluate the treatment
alternatives and their risks and rewards. With the clinical
outcomes modeled and the results displayed with respective
confidence intervals or levels for each outcome related to the
particular treatment options, the medical personnel can make the
appropriate decisions for treating the patient with a balanced
trade-off of parameters important to them. Importantly, the medical
personnel's decision can be made from actual patient data relative
to a similar condition that represents their particular patient's
problem.
Referring now to FIGS. 20-22, shown therein is are methods for
obtaining and analyzing patient information for diagnosing and
treating a patient according to another embodiment of the present
disclosure. Referring more specifically to FIG. 20, shown therein
is a flow chart illustrating a method of collecting and assessing
data associated with diagnosing a patient and selecting available
treatment options for the patient according to another embodiment
of the present disclosure.
Referring more specifically to FIG. 20, shown therein is a flow
chart illustrating a method 300 for diagnosing a patient,
identifying available treatment options for the patient, selecting
a treatment option for the patient, and performing the selected
treatment option according to another embodiment of the present
disclosure. The method 300 begins at step 302 when the patient
enters with complaints indicative of a medical condition. Based on
the types of complaints the patient has, the method 300 continues
at step 304 by categorizing the patient. In that regard, in some
embodiments categorizing the patient comprises identifying one or
more predetermined categories that are associated with the symptoms
or complaints indicated by the patient. The predetermined
categories are provided to the treating physician in some
instances. In other instances, the treating physician or other
medical personnel at least partially defines the categories. In
some embodiments, the categories are at least partially defined or
organized as set forth in FIG. 22 discussed in greater detail
below. Generally, each category defines a series or set of data
points that are useful in evaluating the patient. For example, in
some embodiments where a patient complains of pain in a bony
region, the data set defined by the category includes obtaining an
x-ray of the problem area. Similar correlations between the
patient's symptoms and the desired medical information and/or tests
associated with that symptom are defined for each category.
After categorizing the patient at step 304, the method 300
continues at step 306 with collecting the data associated with each
category in which the patient has been categorized. Accordingly,
the extent of the data collection will vary depending on the
categorization of the patient at step 304. In some instances, at
least some of the data collection is provided by the patient's
primary care physician or referring physician as indicated by step
308. In that regard, the patient has often previously undergone
testing and/or imaging included in the categorization data. In some
instances, this information is provided from the prior medical
personnel to the current medical personnel over a
telecommunications network, such as the internet, phone system,
fax, or otherwise. In one particular embodiment, the data is stored
in a database accessible by the current medical personnel.
In addition to any data that is available from previous medical
personnel, the remaining data that is suggested to be collected for
each category is obtained from the patient. The data may include
standard information on the current physical characteristics (e.g.,
height, weight, mobility, etc.) and condition of the patient.
Further, the data may include goals as to post-treatment mobility,
activity, or relative deformity of the patient. At least some of
the data is obtained by determining the answers to a series of
diagnostic questions defined by each category. In one particular
category, the diagnostic questions include questions such as: How
far can the patient walk without pain? Does the patient have pain
lying down? Does the patient have pain sitting? Does the patient
have pain standing? Does the patient have back pain with leg pain?
If yes, is the leg pain localized or radiating? These are exemplary
questions and are not to be considered limiting. Numerous other
questions may be utilized depending on the categorization of the
patient. In addition, the questions within each category may be
nested such that subsequent questions depend on the answers to
previous questions. Further, some or all of the questions may be
weighted so as to emphasize one or more factors associated with a
category. That is, particular questions and the resultant
information provided by the answers to those questions are given
more importance than other questions and answers. In that regard,
some questions and/or data will be optional for a particular
category. In some instances, the treating physician or medical
personnel may determine the questions and/or data to be included in
each category. In some embodiments, the questions of associated
with each category are provided to the patient and/or medical
personnel in an interactive computer program.
In addition to or in lieu of the diagnostic questions for each
category, the data collection step 306 may include other types of
patient analysis depending on the category. For example, in some
embodiments imaging techniques are utilized to obtain additional
data regarding the patient. For example, in some embodiments
radiographic images of the patient's anatomy are obtained. The
radiographic images are then analyzed to identify the relevant data
associated with the categorization of the patient. In some
embodiments, the patient's motion sequence and/or range of motion
in one or more anatomical areas is a data point to be considered in
evaluating the patient. Accordingly, in some embodiments the
patient is put through a series movements appropriate to determine
the patient's motion sequence and/or range of motion in the one or
more anatomical areas.
In other embodiments, the imaging study includes obtaining patient
images through the use of magnetic resonance imaging ("MRI"),
computed tomography ("CT"), video fluoroscopy, and/or other imaging
techniques. In some embodiments, the imaging study includes
techniques as described in commonly owned U.S. patent application
Ser. No. 11/697,426 filed Apr. 6, 2007 and titled "System and
Method for Patient Balance and Position Analysis", herein
incorporated by reference in its entirety. In general, the imaging
study obtains images of the patient's anatomy that are utilized to
obtain data points as suggested by the categorization of the
patient in step 304.
In some embodiments, the imaging study includes tracking the
movement of anatomical features of the patient using sensors. In
some embodiments the sensors are implantable and are placed in
direct contact with and/or within the relevant anatomical
feature(s) of the patient. In other embodiments, the sensors remain
outside of the patient's body, but are positioned in close
proximity to the anatomical feature(s) of interest. For example, in
some embodiments the imaging study tracks the position of at least
some of a patient's vertebrae. In one embodiment, a sensor is
implanted into each vertebra and the location of the vertebra is
tracked using the sensor. In another embodiment, a sensor is placed
outside the patient's body adjacent the spinous process. The
location of the spinous process and, in turn, the vertebra are
tracked using the sensor. In some embodiments, the position of the
sensors and anatomical features are tracked while the patient is
put through a particular motion sequence or protocol. For example,
in one embodiment the patient is asked to walk on a treadmill. The
position of the sensors and anatomical features are tracked and
correlated to the patient's gait cycle. It is understood that these
described uses of sensors are merely exemplary and should not be
considered limiting. Sensors, implantable or otherwise, may be
utilized in numerous other combinations and ways to track the
position of anatomical features during the imaging study.
Additional questions, imaging, and/or tests are utilized to obtain
data regarding the patient during step 306 as determined by the
treating physician or other medical personnel.
After the relevant data has been collected at step 306, the method
300 continues at step 310 by providing the data to one or more
software applications. In some embodiments, the answers to any
questions prompted by the categorizations are input directly into
the relevant software application. With respect to the imaging
data, in some embodiments the data from the imaging study is
provided to one or more software applications in order to derive
further information and/or new views of the imaging data. Various
brands or types of software for obtaining, analyzing, or otherwise
handling patient data may be used for one or more of the data
categories. Also, multiple software applications may be applied to
a given set of data. It is understood that data from each study can
be assembled together prior to submission to such software, or each
study can be treated individually. In some embodiments, the
Montreal software is used to generate a three-dimensional model of
the patient's spine, the TruBalance software is used to calculate a
global balance for the patient, and the DRPro software is used to
measure the images. In some embodiments, the images are provided to
software known as Clindexia for measuring the images. These
software applications can transform the raw images into
mathematical or other forms that can be utilized by other software
applications and/or manipulated via a computer system and compared
to other images and/or other data sets.
After the data has been provided to the respective software
application(s) at step 310, the method 300 continues with step 312
in which the software application(s) analyzes the data. Generally,
the software application synthesizes the information obtained in
step 306 to identify any abnormal medical conditions afflicting the
patient. In some embodiments, the analysis of the data includes
creating a 3-D and/or 2-D animated model of the patient's anatomy.
This model may be visually represented, such as on a computer
screen or otherwise, in some embodiments. Generally, the animated
model is substantially based on the data obtained in step 306. In
some embodiments, the animated model is used to highlight the
problem areas and/or times in the patient's anatomical motion
sequence. In that regard, in some instances the model includes
layers of anatomical features that are selectively included or
removed. For example, in one embodiment the patient's motion
anatomy is grouped into layers according to the various types of
anatomical tissue, such as bones, cartilage, ligaments, tendons,
muscles, and/or combinations thereof. The animated model then
analyzes motion according to each grouping of anatomical tissue and
the interactions therebetween.
In some embodiments, the animated model combines diagnostic tests
with the imaging study. For example, in some embodiments the
animated model combines muscle monitoring with the imaging study to
identify muscle contractions and tensions during a motion sequence
or protocol. The results of the muscle monitoring are combined with
the other imaging data to provide additional details and/or realism
to the animated model. In other embodiments, the animated model
utilizes center-of-balance or center-of-gravity data for the
patient obtained during the motion sequence or protocol. Muscle
monitoring and center-of-balance data are merely examples of the
types of additional data that may be combined with the imaging data
in forming the animated model. Other types of the patient data may
also be utilized. In that regard, in some embodiments the treating
physician or medical personnel selects the types of patient data to
be used in formulating the animated model.
The animated model includes additional features to allow medical
personnel and/or a computer system to analyze the patient. In that
regard, in some embodiments the animated model includes a stress
grid overlay that indicates potential areas of increased stress or
strain on the patient's anatomy, such increased muscle activity;
overstretching of muscles, ligaments, and/or tendons; friction
between bones; and/or other areas of stress/strain. In some
embodiments the model allows for zooming, panning, or otherwise
changing the orientation of the view of the patient's anatomy.
Users can adjust the orientation of the model relative to
particular anatomical features to better observe or isolate a
potential problem area. Similarly, the animated model allows a user
to pause, rewind, slow down, and/or speed up simulation of a motion
sequence to better observe a potential problem. Further, the
animated model allows 3-D and/or 2D tracking of specific anatomical
features through the motion sequences. In some embodiments, the
software application that creates the animated model also
highlights potential problem areas automatically based on a
comparison to a standardized model. In other embodiments, the
treating physician or medical personnel notes the potential problem
areas based on their own observations. In some embodiments, the
problem areas are identified by the software application and/or
medical personnel by recognizing an abnormal motion pattern(s). In
some embodiments, the model is utilized internally by the software
application to identify the patient's potential medical problems,
but no visual representation of the model is created.
After the software application analyzes the data at step 312, the
method 300 continues with step 314 where an analysis summary and
accompanying statistics are provided. Generally, the summary
provides information important to the diagnosis and subsequent
treatment of the patient's medical condition. In some embodiments,
the summary identifies such things as damaged anatomical features
or areas, limited ranges of motion, and/or other data related to
the patient's medical condition. The information provided by the
summary is at least partially determined by categorization of the
patient in step 304 and may be further defined by the medical
personnel. For example, in some embodiments the medical personnel
selects or otherwise accesses the particular information or data
sets they deem to be most important in diagnosing and treating the
patient. The statistical summary also provides a list of possible
causes for the medical condition and/or identifies possible
relationships between abnormal motion patterns in some
embodiments.
After the analysis summary has been provided at step 314, the
method 300 continues with step 316 where the analysis summary is
compared to a prior patient data set. In that regard, there are
multiple types of prior patient data sets that may be used. The
particular prior patient data set utilized is determined by the
availability of the data sets and/or physician preference. In some
embodiments, the multiple prior patient data sets are groupings
within a single larger data set. In other embodiments, the prior
patient data sets are unrelated, individual data sets. Examples of
the different types of prior patient data sets include a particular
physician's own prior patients; an aggregated collection of
patients from multiple physicians, hospitals, and/or studies;
patients from specific medical personnel, such as a renowned
physician, a mentor, a consultant, or other medical personnel;
and/or a patient wizard using a probabilistic matching system
(i.e., grouping of patients with similar attributes to the current
patient). In some embodiments, the treating physician or other
medical personnel at least partially defines or selects the
parameters of the prior patient data set to be used. In some
embodiments, the prior patient data set is a collection of prior
patients having similar medical conditions, medical histories,
and/or patient profiles to the current patient. The prior patient
data sets include the selected treatment plans and relative success
of those plans for the prior patients. Accordingly, the current
patient's physical characteristics and attributes can be compared
to prior patients with similar characteristics and attributes.
Then, the one or more treatment options that have been successful
for prior patients with characteristics and attributes similar to
the current patient may be identified.
After the patient analysis summary has been compared to the prior
patient data set(s) at step 316, the method 300 continues with the
selection of a appropriate treatment option(s) at step 318. In some
embodiments, the comparison of the patient analysis summary and the
prior patient data will identify a single treatment plan that is
clearly considered best for the patient. However, in other
embodiments a plurality of treatment plans are identified by the
comparison as possible treatment plans for the current patient. In
such embodiments, the plurality of treatment options are sorted
and/or screened to further narrow the treatment options. In some
instances, the treatment options are screened based on physician
preference. For example, if a physician prefers a particular
surgical procedures or approach, then the available treatment
options are limited to those that utilize the preferred procedure
or approach. As another example, the treatment options are sorted
or ranked based on the likelihood of success of the treatment plan
based on the data for previous patients having a similar profile to
the current patient. Similarly, in some embodiments the treatment
options are sorted or ranked based on the previous procedures
performed by the treating physician/surgeon and the relative
success of those procedures. Based on the comparisons to prior
patient data, physician preferences, and likelihood of success a
specific treatment plan is selected for the patient.
Referring more specifically to FIG. 21, shown therein is a flow
chart illustrating a method 320 for data collection and analysis
according to another embodiment of the present disclosure that is
used in conjunction with a method of diagnosing a patient,
identifying available treatment options for the patient, selecting
a treatment option for the patient, and performing the selected
treatment option, such as method 300 described above. In that
regard, some of the steps of the method 320 are substantially
similar to some of the steps of the method 300 where the steps of
the method 300 are focused on data collection and/or data
analysis.
The method 320 begins at step 322 when the patient enters with
complaints indicative of a medical condition. The method 320
continues at step 324 where the patient is asked a series of
questions and/or is otherwise assessed. Generally, the questions
and/or assessment will be focused around the physical symptoms
associated with the patient's complaints. These initial questions
are focused on identifying potential medical problems of the
patient. Additional questions and information will be obtained
based on the answers to the questions of step 324. For example, in
the current embodiment the method 320 continues at step 326 where
the appropriate diagnostic recommendations are determined. The
diagnostic recommendations comprise the medical tests, imaging,
and/or additional questions that the patient should be put through
based on the answers to the initial questions/assessment of step
324.
In some embodiments, the diagnostic recommendations are based on
grouping the patient into categories based on the responses of step
324. In that regard, in some embodiments categorizing the patient
comprises identifying one or more predetermined categories that are
associated with the symptoms or complaints indicated by the
patient. The predetermined categories are provided to the treating
physician in some instances. In other instances, the treating
physician or other medical personnel at least partially define the
categories. In some embodiments, the categories are at least
partially defined or organized as set forth in FIG. 22.
Referring more specifically to FIG. 22, shown therein is a
diagrammatic schematic view of a data structure 342 for use with
the methods 300, 320 according to one embodiment of the present
disclosure. Generally, the data structure 342 comprises a series of
categories 344 that each define a corresponding data set 346. Each
data set 346, in turn, comprises a plurality of data points or
items 348. The items 348 represent the specific data, images,
answers to questions, etc. that are recommended to be obtained for
each category 344. In that regard, the categories 344 may each be
associated with a particular patient symptom or complaint. For
example, Category 1 (block 350) may represent a particular patient
symptom such as lower back pain. In turn, the Data Collection Set 1
(block 352) comprises a plurality of items, namely Item 1 (block
354), Item 2 (block 356), and so on to Item X (block 358). Each
Item 1, 2, and X (354, 356, 358) represents a specific data point,
image, answer to a question, or other information that is
recognized as being beneficial in diagnosing the medical condition
of a patient having lower back pain. The items in each category
represent data or information that may be useful in evaluating and
diagnosing the patient. For example, in some embodiments where a
patient complains of pain in a bony region, the data collection set
defined by the category includes an item that requires obtaining an
x-ray of the problem area. Similar correlations between the
patient's symptoms and the desired medical information and/or tests
associated with that symptom are defined for each of the categories
of the data structure 342.
In some instances, some of the Items 1-X of the Data Collection Set
1 (block 352) are optional. That is, some of the items included in
the Data Collection Set 1 (block 352) are not necessary for
diagnosing the patient, but may be beneficial in some instances.
Similarly, some of the items included in the Data Collection Set 1
(block 352) are necessary for a proper diagnoses of the patient
and, therefore, should always be obtained. In some embodiments, the
required and optional items are predetermined and stored within a
software application for each category. In some embodiments, the
treating physician or medical personnel determines and/or modifies
what items are required and/or optional for a specific category. In
some embodiments, the items are weighted by importance for each
category. That is, items may not be given a required or optional
label, but rather will be rated based on the relative importance
and/or benefit of the item to the diagnosis of the patient. As
shown, the data structure 342 includes a plurality of categories
each with its own data collection set. For example, the current
data structure 342 includes Category 1 (block 350) and its
corresponding Data Collection Set 1 (block 352); Category 2 (block
360) and its corresponding Data Collection Set 2 (block 362);
through Category Y (block 364) and its corresponding Data
Collection Set Y (block 366).
Referring again to FIG. 21, step 326 also includes obtaining the
diagnostic recommendations. For example, after grouping the patient
into one or more of the categories 344, the items 348 associated
with each of the categories are obtained. Accordingly, the extent
of the data collection/diagnostic recommendations will vary
depending on the categorization of the patient. In some instances,
at least some of the data collection is provided by the patient's
primary care physician or referring physician. In that regard, the
patient has often previously undergone testing and/or imaging that
are included in the item lists of the categories. In some
instances, this information is provided from the prior medical
personnel to the current medical personnel over a
telecommunications network, such as the internet, phone system,
fax, or otherwise. In one particular embodiment, the data is stored
in a database accessible by the current medical personnel. In
addition to any data that is available from previous medical
personnel, the remaining items that are suggested to be collected
for each category are obtained from the patient. These items may
include standard information on the current physical
characteristics (e.g., height, weight, mobility, etc.) and
condition of the patient. Further, the items may include goals as
to post-treatment mobility, activity, or relative deformity of the
patient. At least some of the items are obtained by determining the
answers to a series of diagnostic questions associated with a
particular category. In that regard, the questions within each
category may be nested such that subsequent questions depend on the
answers to previous questions. Further, some or all of the items
may be weighted so as to emphasize one or more factors associated
with a particular category. That is, particular items and the
resultant information provided thereby are given more importance
than diagnostic items.
The data collection sets include various types of items depending
on the category. For example, in some embodiments imaging
techniques are utilized to obtain additional data regarding the
patient. In particular, in some embodiments radiographic images of
the patient's anatomy are obtained. The radiographic images are
then analyzed to identify the relevant data associated with a
particular item. In some instances, the radiographic or other
images comprise the item to be collected. In some embodiments, the
patient's motion sequence and/or range of motion in one or more
anatomical areas is an item to be obtained in evaluating the
patient. Accordingly, in some embodiments the patient is put
through a series movements appropriate to determine the patient's
motion sequence and/or range of motion in the one or more
anatomical areas. In other embodiments, the items include obtaining
patient images through the use of magnetic resonance imaging
("MRI"), computed tomography ("CT"), video fluoroscopy, and/or
other imaging techniques. In general, the imaging obtains images of
the patient's anatomy that are utilized to obtain data points or
items as set forth in the data collection set for each category
associated with the patient.
After the relevant informational items have been determined and
collected at step 326, the method 320 continues at step 328 where
the data is analyzed. In some embodiments, the data is provided to
one or more software applications for analysis. In that regard, in
some embodiments the answers to questions included in the item list
for categorizations are input directly into the relevant software
application. With respect to the imaging data, in some embodiments
the data from the imaging is provided to one or more software
applications in order to derive further information and/or new
views of the imaging data. Various brands or types of software for
obtaining, analyzing, or otherwise handling patient data may be
used for one or more of the data categories. Also, multiple
software applications may be applied to a given set of item data.
It is understood that data from each study can be assembled
together prior to submission to such software, or each study can be
treated individually. In some embodiments, these software
applications transform the raw images into mathematical or other
forms that can be utilized by other software applications and/or
manipulated via a computer system and compared to other images
and/or other data sets.
Generally, the software applications synthesize the information to
identify any abnormal medical conditions afflicting the patient. In
some embodiments, the analysis of the data includes creating a 3-D
and/or 2-D animated model of the patient's anatomy. This model may
be visually represented, such as on a computer screen or otherwise,
in some embodiments. Generally, the animated model is substantially
based on the data obtained in step 306. In some embodiments, the
animated model is used to highlight the problem areas and/or times
in the patient's anatomical motion sequence. In that regard, in
some instances the model includes layers of anatomical features
that are selectively included or removed. For example, in one
embodiment the patient's motion anatomy is grouped into layers
according to the various types of anatomical tissue, such as bones,
cartilage, ligaments, tendons, muscles, and/or combinations
thereof. The animated model then analyzes motion according to each
grouping of anatomical tissue and the interactions
therebetween.
In some embodiments, the animated model combines diagnostic tests
with the imaging study. For example, in some embodiments the
animated model combines muscle monitoring with the imaging study to
identify muscle contractions and tensions during a motion sequence
or protocol. The results of the muscle monitoring are combined with
the other imaging data to provide additional details and/or realism
to the animated model. In other embodiments, the animated model
utilizes center-of-balance or center-of-gravity data for the
patient obtained during the motion sequence or protocol. Muscle
monitoring and center-of-balance data are merely examples of the
types of additional data that may be combined with the imaging data
in forming the animated model. Other types of the patient data may
also be utilized. In that regard, in some embodiments the treating
physician or medical personnel selects the types of patient data to
be used in formulating the animated model.
The animated model includes additional features to allow medical
personnel and/or a computer system to analyze the patient. In that
regard, in some embodiments the animated model includes a stress
grid overlay that indicates potential areas of increased stress or
strain on the patient's anatomy, such increased muscle activity;
overstretching of muscles, ligaments, and/or tendons; friction
between bones; and/or other areas of stress/strain. In some
embodiments the model allows for zooming, panning, or otherwise
changing the orientation of the view of the patient's anatomy.
Users can adjust the orientation of the model relative to
particular anatomical features to better observe or isolate a
potential problem area. Similarly, the animated model allows a user
to pause, rewind, slow down, and/or speed up simulation of a motion
sequence to better observe a potential problem. Further, the
animated model allows 3-D and/or 2D tracking of specific anatomical
features through the motion sequences. In some embodiments, the
software application that creates the animated model also
highlights potential problem areas automatically based on a
comparison to a standardized model. In other embodiments, the
treating physician or medical personnel notes the potential problem
areas based on their own observations. In some embodiments, the
problem areas are identified by the software application and/or
medical personnel by recognizing an abnormal motion pattern(s). In
some embodiments, the model is utilized internally by the software
application to identify the patient's potential medical problems,
but no visual representation of the model is created.
After the data has been analyzed at step 328, the method 320
continues with step 330 where the patient is classified based on
the identified medical conditions. In that regard, the patient is
classified based on the data/results provided in response to the
items obtained in the data collection sets. Generally, the patient
is classified based on information that is important to the
diagnosis and subsequent treatment of the patient's medical
condition. In some embodiments, the patient is classified based on
such things as the damaged anatomical features or areas, extent of
limited range of motion, and/or other data related to the patient's
medical condition.
After the classification of the patient at step 330, the method 320
continues with step 322 where a treatment plan is recommended for
the patient. In some instances, the patient's data is compared to a
prior patient data set for determining an appropriate treatment
plan. In that regard, there are multiple types of prior patient
data sets that may be used. The particular prior patient data set
utilized is determined by the availability of the data sets and/or
physician preference. In some embodiments, the multiple prior
patient data sets are groupings within a single larger data set. In
other embodiments, the prior patient data sets are unrelated,
individual data sets. Examples of the different types of prior
patient data sets include a particular physician's own prior
patients; an aggregated collection of patients from multiple
physicians, hospitals, and/or studies; patients from specific
medical personnel, such as a renowned physician, a mentor, a
consultant, or other medical personnel; and/or a patient wizard
using a probabilistic matching system (i.e., grouping of patients
with similar attributes to the current patient). In some
embodiments, the treating physician or other medical personnel at
least partially defines or selects the parameters of the prior
patient data set to be used. In some embodiments, the prior patient
data set is a collection of prior patients having similar medical
conditions, medical histories, and/or patient profiles to the
current patient. The prior patient data sets include the selected
treatment plans and relative success of those plans for the prior
patients. Accordingly, the current patient's physical
characteristics and attributes can be compared to prior patients
with similar characteristics and attributes. Then, the one or more
treatment options that have been successful for prior patients with
characteristics and attributes similar to the current patient may
be identified.
After a treatment plan has been recommended at step 332, the method
320 continues with the selection of a appropriate treatment plan at
step 334. In some embodiments, the comparison of the patient
analysis summary and the prior patient data will identify a single
treatment plan that is clearly considered best for the patient. In
such instances, the single treatment plan will be selected at step
334. However, in other embodiments a plurality of treatment plans
are identified by the comparison as possible treatment plans for
the current patient. In such embodiments, the plurality of
treatment options are sorted and/or screened to further narrow the
treatment options. In some instances, the treatment options are
screened based on physician preference. For example, if a physician
prefers a particular surgical procedures or approach, then the
available treatment options are limited to those that utilize the
preferred procedure or approach. As another example, the treatment
options are sorted or ranked based on the likelihood of success of
the treatment plan based on the data for previous patients having a
similar profile to the current patient. Similarly, in some
embodiments the treatment options are sorted or ranked based on the
previous procedures performed by the treating physician/surgeon and
the relative success of those procedures. Based on the comparisons
to prior patient data, physician preferences, and likelihood of
success a specific treatment plan is selected for the patient.
After selecting a treatment plan at step 334, the method 320
continues at step 336 by discussing and/or educating the patient
about the selected treatment option. In that regard, the results of
the analyses and modeling (if performed) are shown and/or explained
to the patient to support the decision to go with a particular
treatment plan. Further, in the case of a treatment plan that
includes inserting an implant or otherwise employing a medical
device, the patient may be given access to additional product
information regarding the medical device. In some embodiments,
discussing the treatment option with the patient is accomplished
over the internet, an intranet, computer network,
telecommunications network, or other type of remote connection. In
that regard, the link between the patient and the medical
professional may be a secure link or secured communication channel
so as to protect the patient's confidentiality. In some instances,
the treatment options are provided over a secure website. The
patient is provided access to the secure website via a username and
password associated with the patient. In addition to providing the
patient information regarding the selected treatment option(s), the
patient interface also provides the patient with the ability to ask
questions. In some embodiments, the interface includes a query box
that is filled out and submitted by the patient, which a medical
professional replies to. In other embodiments, the interface is in
the form of a chat or instant messaging session. The patient may
ask questions over the chat session and the medical personnel can
provide answers to these questions immediately or seek answers to
the questions and reply to the patient at a later time. In yet
other embodiments, the patient interface may be combined with
video-conferencing or telephonic-conferencing to provide additional
information and opportunities for questions to the patient.
After selection of the treatment option at step 334 and education
of the patient at step 336, the method 320 continues with step 338
in selected treatment plan is executed. The treatment plan is
executed in accordance with the planning that has occurred in the
previous steps or as part of step 338. In that regard, in some
instances of a surgical procedure the procedure is monitored
intra-operatively to ensure compliance with the planned procedure.
The actual surgical procedure, as monitored, is compared in
real-time, or approximately real-time, to the planned treatment.
Thus, the actual placement of an implant and/or fixation devices is
compared to the intended placement and/or associated error fields
as defined in the treatment plan. In this manner, an analysis of
the placement of the surgical components is performed before the
patient leaves the operating room. In that regard, the actual
surgical procedure is modified as needed to ensure that it
coincides with the error fields of the planned treatment. Any
adjustments that need to be made to comply with the treatment plan
can be accomplished without the need for a revision surgery or a
return to the operating room.
After executing the treatment plan or at least a part thereof at
step 338, the method 320 continues with step 340 in which a
post-treatment, follow-up analysis is performed. In some
embodiments, the post-treatment analysis is substantially similar
to steps 324, 326, and/or 328 described above. In some embodiments,
the post-treatment analysis step 340 includes comparing the
predicted results of the treatment plan to the actual results of
the treatment. Any discrepancies are identified and utilized to
improve the correlation between the predicted results and the
actual results of the treatment plan, as indicated by the feedback
loop of step 341. In that regard, in some embodiments the
parameters utilized for creating the models are updated and
modified based on the identified discrepancies. Ideally, the
predicted results are substantially similar to the actual results
of the treatment plan. In some embodiments, the post-treatment
analysis is performed at set intervals after the initial treatment.
In one particular embodiment, the patient goes through
post-treatment analysis at least at 2 weeks, 6 weeks, and 3 month
intervals after the initial treatment.
By monitoring the resultant data from each patient for each
treatment plan, a statistical correlation between medical
conditions and treatment options is established. This statistical
correlation is utilized in selecting the treatment plans for
subsequent patients. The current patient's resultant data is routed
and stored as a part of a study and/or other collection of data
into a database for future access. Generally, the data will be
de-identified from the particular patient, so as to preserve
confidentiality and impartially of the data and to comply with
applicable privacy laws. For example, the patient's name, social
security number, address, and/or other sensitive information are
removed from the data, while the patient's physical
characteristics, selected treatment plan, and outcome are
maintained. In some embodiments, the data is entered into the
database(s) by a medical professional as part of the post-treatment
analysis. The data related to current patient's outcome creates a
feedback loop that provides confirmation of prior information
and/or new information from which the medical professionals can
modify the treatment plans and/or medical device manufacturers can
modify the implants or devices.
In some instances, the patient data, images, models, simulations,
and/or other information of the present disclosure are processed,
compiled, or otherwise manipulated. In that regard, the methods,
systems, and concepts described in the following references are
utilized in connection with the patient data, images, models,
simulations, and/or other information in some instances: U.S. Pat.
No. 5,970,499 filed Apr. 11, 1997 and titled "Method and Apparatus
for Producing and Accessing Composite Data"; U.S. Pat. No.
6,009,212 filed Jul. 10, 1996 and titled "Method and Apparatus for
Image Registration"; U.S. Pat. No. 6,226,418 filed Nov. 5, 1998 and
titled "Rapid Convolution Based Large Deformation Image Matching
Via Landmark and Volume Imagery"; U.S. Pat. No. 6,253,210 filed
Aug. 25, 1999 and titled "Method and Apparatus for Producing and
Accessing Composite Data"; U.S. Pat. No. 6,408,107 filed Nov. 14,
2000 and titled "Rapid Convolution Based Large Deformation Image
Matching Via Landmark and Volume Imagery"; U.S. Pat. No. 6,526,415
filed Jun. 11, 2001 and titled "Method and Apparatus for Producing
an Accessing Composite Data"; U.S. Pat. No. 6,553,152 filed Apr.
27, 1999 and titled "Method and Apparatus for Image Registration";
U.S. Pat. No. 6,611,630 filed Jun. 7, 1999 and titled "Method and
Apparatus for Automatic Shape Characterization"; U.S. Pat. No.
6,633,686 filed Sep. 20, 2000 and titled "Method and Apparatus for
Image Registration Using Large Deformation Diffeomorphisms on a
Sphere"; U.S. Pat. No. 6,694,057 filed Jan. 27, 2000 and titled
"Method and Apparatus for Processing Images with Curves"; U.S. Pat.
No. 6,708,184 filed May 4, 2001 and titled "Method and Apparatus
for Producing and Accessing Composite Data Using a Device Having a
Distributed Communication Controller Interface"; U.S. Pat. No.
6,754,374 filed Dec. 16, 1999 and titled "Method and Apparatus for
Processing Images with Regions Representing Target Objects"; each
of which is hereby incorporated by reference in its entirety.
Referring now to FIG. 23, shown therein a method 400 for
visualizing and analyzing anatomical motion according to one
embodiment of the present disclosure. Generally, the method 400
utilizes sensors, wireless telemetry or other communication means,
and 3-D or 2-D reconstructions of the anatomy to visualize and
analyze the anatomical motion. As described in greater detail
below, the method 400 is for use in patient treatment. For example,
in various embodiments the method 400 is used for diagnosing and/or
categorizing a patient's medical problems, creating a patient
treatment plan (e.g., surgical procedures, physical therapy,
chemical therapy (e.g., pharmaceuticals or other drug therapies),
and combinations thereof), monitoring the progress of a patient
treatment plan, comparing the effectiveness of different treatment
plans for patients with similar medical problems, and numerous
other medical applications. Further, the method 400 is particularly
well suited for use in orthopedic applications. For example, in one
particular embodiment the method 400 is used in the analysis and
treatment of spinal disorders. As another example, the method 400
is also used in the analysis and treatment of patients likely to
receive prosthetic joint replacements (e.g., hip, knee, vertebrae,
and ankle) in other embodiments. In such embodiments, the method
400 is configured to provide information useful in determining the
appropriate prosthetic implant for a patient (e.g., shape, size,
design, material, etc.) and is further configured to monitor the
effectiveness of the prosthetic after implantation in some
instances.
The method 400 begins at step 402 in which one or more sensors are
introduced. In some embodiments, the sensors are accelerometer
and/or gyroscopes. In particular, in some embodiments the sensors
comprise a micro-accelerometer. In some aspects, the
micro-accelerometer is either MEMS-based or piezoelectric-based.
MEMS-based micro-accelerometers are preferred in some instances
because there is no need for motion to obtain useable data.
Generally, the sensors are placed in close proximity to an
anatomical structure of interest. In this manner, the sensors are
utilized to correlate the position of the anatomical structure
based on the motion data obtained from the sensor. In some
instances, a plurality of sensors may be utilized adjacent to a
single anatomical feature to provide more accurate position data
for the anatomical structure and/or provide redundancy. In some
instances, position information is extrapolated using secondary
systems in the sensor device. For example, in some instances a
wireless communications interface used for sending data between the
sensors and a processing unit can be used to detect the relative
distances between the sensors and the processing unit through
ping-response time measurements. The sensors may be implanted into
the patient's body adjacent to the anatomical feature of interest,
placed on the skin of the patient adjacent to the anatomical
feature(s) of interest, and/or placed on clothing of the patient
adjacent to the anatomical feature(s). In some embodiments,
implantable sensors are preferred. In some instances, sensors are
used both inside and outside of the patient's body. Implantable
sensors facilitate direct contact with the anatomical feature(s) of
interest or at least provide substantially closer placement to the
anatomical features than sensors that remain outside the patient's
body. In that regard, implantable sensors facilitate the accurate
detection of the position of internal anatomical features that
cannot be accurately determined with external sensors alone.
In some embodiments, the implantable sensors are configured for
engagement with bone. In that regard, the implantable sensors are
part of a bone screw or other bone fixation device in some
embodiments. In other embodiments, the implantable sensors are
secured to the bone via a biocompatible adhesive, a structural
fixation device (screw, staple, etc.), combinations thereof, and/or
other otherwise secured to the bone. Generally, engaging the
implantable sensors with bone provides a fixed orientation between
the sensor and the bone, which allows a good correlation between
the position of the sensor and the position of the bone. In other
embodiments, the sensors are configured for engagement with softer
tissues. In such embodiments, the sensors include features to
prevent unwanted movement of the sensors relative to the tissue.
Where the sensors are implanted inside the body, the sensors are
introduced via a guidewire, needle, catheter, tube, and/or other
suitable implantation means. Preferably, the sensors are implanted
using a minimally invasive procedure and in some instances are
implanted percutaneously. In some embodiments, systems and methods
may be used as described in U.S. patent application Ser. No.
10/985,108 filed Nov. 10, 2004 and titled "Method and Apparatus for
Expert System to Track and Manipulate Patients," herein
incorporated by reference in its entirety.
The sensors are utilized for tracking the position of one or more
anatomical features. In that regard, one or more sensors are placed
adjacent each anatomical feature of interest. In some embodiments
the sensors are configured for identifying the location of one or
more of the following anatomical features or parts thereof: heels,
ankles, knees, hips, iliac crests, sacrum, pelvis, spinal column,
spinal column regions, vertebrae, transverse processes, spinal
processes, clavicles, and other anatomical features. In one
particular embodiment, the sensors are placed on a plurality of
vertebrae. As will be described in greater detail below, the
relative motion of the sensors placed on each of the plurality of
vertebrae are utilized to obtain relative orientation and motion
information for the vertebrae. The actual anatomical features for
which sensors are located adjacent to depends on numerous factors
including physician preference, patient condition, treatment plans,
surgical procedures, and other factors. In some embodiments, the
anatomical feature(s) of interest may be selected by the treating
physician or technician.
After the sensors have been introduced at step 402, the method 400
continues at step 404 in which an imaging protocol is performed. In
orthopedic applications, the imaging focuses on the relevant
skeletal structures of the patient. Generally speaking, the imaging
of step 404 may include x-ray, fluoroscopy, and/or CT scans. X-ray
machines may be utilized to obtain snap-shot images of the
patient's skeletal structure. Fluoroscopy machines may be utilized
to obtain real-time images of the patient's skeletal structure. In
some embodiments, the imaging step 404 is utilized to obtain images
of the patient's spinal column, pelvis, iliac crest, sacrum, hips,
shoulders, clavicles, skull, arms, legs, knees, ankles, feet,
and/or combinations thereof. In some embodiments, the imaging
protocol is utilized to obtain at least sagittal and frontal images
of the patient's anatomy. In some embodiments, the patient simply
turns to obtain the desired perspective view for the radiograph. In
that regard, the patient may be asked to physically turn herself or
himself or, in some embodiments, a moveable platform rotates the
patient between the desired positions such that the patient can
remain substantially stationary between positions. In some
embodiments the imaging step 404 simultaneously obtains the
sagittal and frontal images of the patient's anatomy. In addition
to the sagittal and frontal views, the other views of the patient's
anatomy that would be advantageous to patient analysis are
obtained.
After imaging protocol has been performed at step 404, the method
400 continues at step 406 in which a model of the patient's
relevant anatomical features is created. Generally, the data from
the imaging protocol is utilized to create the model. In one
particular embodiment, the data from the imaging protocol is
utilized to segment the model into the individual bones of the
patient. In that regard, a joint is modeled by the combination of
individual bones that come together to form the joint. In some
embodiments, the dimensions of the implanted sensor are known and
utilized to correlate bone position to the sensor position.
Further, the orientation of the sensor to the bone is established
by an asymmetry in the structure of the sensor that is identifiable
through the imaging protocol. Accordingly, in some embodiments the
known dimensions and features of the implanted sensors are utilized
in creating the model of the patient's anatomical features. The
model is either a 3-D or 2-D representation of the patient's
anatomy. In some embodiments, the model is animated to illustrate a
motion sequence of the patient's anatomy. The animated model is
particular beneficial in the diagnosis and treatment of orthopedic
joints. One particular method for modeling the patient's anatomy is
to provide or develop a highly accurate model of a generic
skeleton, and then map a model of the specific patient derived from
an imaging study to the generic skeleton. In some instances this is
accomplished through identifying key landmarks on each bone, and
then growing or shrinking the original master model according to
the measured distances of these landmarks on the patient. Through
this method, a useful 3D model of a patient is created that can
then undergo kinematics and/or finite element analysis. In some
instances, the modeling is performed in a manner similar to that
described by Rajamani, K. T.; Joshi, S. C.; Styner, M. A., "Bone
model morphing for enhanced surgical visualization," Biomedical
Imaging: Nano to Macro, 2004. IEEE International Symposium on,
vol., no., pp. 1255-1258 Vol. 2, 15-18 Apr. 2004, hereby
incorporated by reference in its entirety.
After creation of the model at step 406, the method 400 continues
at step 408 with the performance of a diagnostic protocol.
Generally, the diagnostic protocol is performed to measure joint
motion and/or relative motion between anatomical features. In a
first aspect, the diagnostic protocol utilizes the relative motion
between the implanted sensors to monitor joint motion. That is, the
movement of each sensor with respect to the other sensors is
tracked and utilized to determine the relative motion between the
anatomical features associated with each sensor. In a second
aspect, the diagnostic protocol utilizes the absolute positions of
the sensors to correlate to the motion of the anatomical features.
That is, the positions of the sensors are tracked with respect to a
reference point (e.g., a signal receiver), which can in turn be
utilized to determine the motion of the anatomical features. In
some embodiments, the positions of the sensors are monitored using
wireless telemetry to measure the distances between each sensor.
For example, in some instances each sensor is registered with a
signal receiver and the position of the sensor is tracked using
wireless telemetry. Based on the communication of the sensor with
the signal receiver a time of flight calculation can be made to
triangulate the position of the sensor with respect to the signal
receiver over time. The positions of each of the sensors can then
be compiled to identify the relative motion sequence of the
anatomical features with respect to one another. Taken together,
the motion of the anatomical features with respect to one another
define the joint motion.
In either case, the relative orientations of the sensors are
initially determined at a static point or a reference point. In
some instances, the relative orientations of the sensors at the
static point are determined by the direction of gravity as measured
by each of the accelerometer sensors. In other instances, the
relative orientations of the sensors are determined by the
positions of the sensors obtained from the telemetry communication
of the sensors with the signal receiver. Once the relative
orientations and/or positions of the sensors have been determined,
the patient is moved through a diagnostic protocol comprising a
series of movements. During the series of movements the
acceleration and/or positional data from the sensors is obtained.
The actual series of movements the patient is put through depends
upon the specific medical condition of the patient. In some
embodiments, the diagnostic protocol comprises having the patient
walk on a treadmill. In some instances, a reference point or time=0
point is established. In that regard, the reference point is a
starting point for identifying the motion sequence of the patient's
anatomical features. Accordingly, in some embodiments the reference
point is established based on an image obtained during the imaging
step 404.
In some embodiments, an electromagnetic measurement system is
utilized to track the positions of the implantable sensors. For
example, the electromagnetic measurement system can detect the
presence of sensors excitable by an electromagnetic field to
determine the position of the anatomical features associated with
the sensor. As described above, the sensors may be external or
implantable. The electromagnetic measurement system may utilize a
computer system to calculate the 3-D position of the anatomical
feature(s) based on the position of the sensors. In some
embodiments, the electromagnetic measurement system is configured
to detect the position of sensors in a fixed volume of space. In
that regard, in some embodiments the fixed volume of the
electromagnetic measurement system is sufficient to obtain the
position of all relevant anatomical features of a patient. In other
embodiments, however, the fixed volume may be sufficient to obtain
3-D positions of only some anatomical features of a patient. Where
the fixed volume is sufficient to obtain 3-D positions of some, but
not all of the patient's anatomical features, a portion of the
electromagnetic measure system (e.g., the electromagnetic field
generator) may be moveable such that the 3-D positions of the
anatomical features of most interest can be obtained. In lieu of or
in addition to the electromagnetic measurement system, an infrared
system and/or a video system are utilized for determining the 3-D
position of the sensors in some embodiments. The video system may
be a single camera or multi-camera system. In that regard, a
multi-camera video system may take the resulting video triangulate
the positions of anatomical features of interest using a computer
system. Video system in this context is understood to include still
photography in addition to moving video.
After the diagnostic protocol of step 408 has been performed, the
method 400 continues at step 410 in which the model of step 406 is
updated and/or a new 3-D and/or 2-D animated model of the patient's
anatomy is created to visualize the patient's anatomy. Generally,
the animated model is based on the data obtained from the imaging
of step 404 and the diagnostic protocol of step 408. In some
embodiments, the animated model is used to highlight the problem
areas and/or times in the patient's anatomical motion sequence. In
that regard, the model includes layers of anatomical features that
are selectively included or removed. For example, in one embodiment
the patient's motion anatomy is grouped into layers according to
types of anatomical tissue, such as bones, cartilage, ligaments,
tendons, muscles, and/or combinations thereof. The animated model
then analyzes motion according to each grouping of anatomical
tissue and the interactions therebetween.
In some embodiments, the animated model combines diagnostic tests
with the imaging study. For example, in some embodiments the
animated model combines muscle monitoring with the imaging study to
identify muscle contractions and tensions during a motion sequence
or protocol. The muscle monitoring is accomplished through the use
of additional sensors in some embodiments. In other embodiments,
the muscle monitoring is accomplished through the use of external
sensing systems. The results of the muscle monitoring are combined
with the other imaging data to provide additional details and/or
realism to the animated model. In other embodiments, the animated
model utilizes center-of-balance or center-of-gravity data for the
patient obtained during the motion sequence or diagnostic protocol.
Muscle monitoring and center-of-balance data are merely examples of
the types of additional data that may be combined with the imaging
data in forming the animated model. Other types of the patient data
may also be utilized. In that regard, in some embodiments the
treating physician or medical personnel selects the types of
patient data to be used in formulating the animated model.
The method 400 continues with step 412 in which the data obtained
from the diagnostic protocol of step 408 is analyzed. In some
embodiments, the animated model includes features to allow medical
personnel and/or a computer system to analyze the patient's motion
sequence. In that regard, in some embodiments the animated model
includes a stress grid overlay that indicates potential areas of
increased stress or strain on the patient's anatomy, such increased
muscle activity; overstretching of muscles, ligaments, and/or
tendons; friction between bones; and/or other areas of
stress/strain. In some embodiments the model allows for zooming,
panning, or otherwise changing the orientation of the view of the
patient's anatomy. A user adjusts the orientation to better observe
or isolate a potential problem area. Similarly, the animated model
allows a user to pause, rewind, slow down, and/or speed up
simulation of a motion sequence to better observe a potential
problem. Further, the animated model allows 3-D and/or 2D tracking
of specific anatomical features through the motion sequences. In
some embodiments, the animated model highlights potential problem
areas automatically based on a comparison to a standardized model.
For example, the system may identify anatomical features with a
motion sequence outside of a predetermined range. In that regard,
the standardized model and/or predetermined range of normal motion
are at least partially defined by a general patient population. In
some embodiments, the treating physician or medical personnel
highlights potential problem areas based on their observations of
the patient's motion sequence. In some embodiments, the problem
areas are identified by a computer system and/or medical personnel
by recognizing an abnormal motion pattern(s). In some instances,
the abnormal motion patterns are grouped into motion signatures
that are indicative of a medical condition. Each of the motion
signatures, in turn, are associated with appropriate medical
treatment options for correcting the medical condition(s)
associated with the motion signature. The method 400 concludes at
step 414 by summarizing the results of the analysis of step
412.
Referring now to FIG. 24, shown therein a method 420 for using
implantable sensors in an image-guided treatment according to one
embodiment of the present disclosure. Generally, the method 420
utilizes implantable sensors as fiducial markers for use during the
image-guided procedure. In this context a fiducial marker provides
a reference point for orientation of implants and surgical
instruments during the image-guided treatment. In some instances,
the sensors are configured to be affixed to portions of a patient's
body, especially the bony anatomy, and are configured to show up in
an x-ray or other imaging so that succeeding scans or pictures may
be registered or correlated to one another. In accordance with the
present disclosure, the implantable sensors are capable of being
mapped in three-dimensional format relative to one another as
described above. Generally, the relative motion of the sensors
between one another may be utilized to track the motion of the
anatomical features of the patient and/or the positions of the
sensors relative to a reference point or receiver may be utilized
to track the motion of the anatomical features. The method 420 is
particularly well suited for use in orthopedic surgical procedures,
such as spinal surgeries, joint replacements, and other orthopedic
procedures. In that regard, the method 420 is configured to provide
positional information useful in ensuring the appropriate placement
and orientation of any implants and/or fixation devices during the
surgical procedure. Further, the implantable sensors are used to
monitor the placement and orientations of the implant and/or
fixation devices after implantation in some instances.
The method 420 begins at step 422 in which one or more sensors are
introduced. In some embodiments, the sensors are accelerometer
and/or gyroscopes. In particular, in some embodiments the sensors
comprise a micro-accelerometer. Generally, the sensors are placed
in close proximity to an anatomical structure of interest. In this
manner, the sensors are utilized to correlate the position of the
anatomical structure based on the position of the sensor. In some
instances, a plurality of sensors may be utilized adjacent to a
single anatomical feature to provide more accurate position data
for the anatomical structure. The sensors may be implanted into the
patient's body adjacent to the anatomical feature of interest
and/or placed on the skin of the patient adjacent to the anatomical
feature(s) of interest. For most procedures, implantable sensors
are preferred. In some instances, sensors are used both inside and
outside of the patient's body. Implantable sensors facilitate
direct contact with the anatomical feature(s) of interest or at
least provide substantially closer placement to the anatomical
features than sensors that remain outside the patient's body. In
that regard, implantable sensors facilitate the accurate detection
of the position of internal anatomical features that cannot be
accurately determined with external sensors alone.
In some embodiments, the implantable sensors are configured for
engagement with bone. In that regard, the sensors may be secured to
the surface of a bone (e.g., using an epoxy or other biocompatible
adhesive), inserted into a void in the bone, press-fit into the
bone, cemented into the bone, and/or imbedded in a housing or
device that is secured to the bone. In some embodiments, the
implantable sensors are part of a bone screw or other bone fixation
device. For example, referring more particularly to FIGS. 25 and
26, shown therein is a bone screw 430 in accordance with one
embodiment of the present disclosure. The bone screw 430 comprises
a head portion 432 and a body portion 434. In the current
embodiment, the body portion 434 is threaded such that it may be
screwed into a bone of a patient. In other embodiments, the bone
screw is secured to the bone via a biocompatible adhesive, surface
coatings or treatments (e.g. chemical etching, bead-blasting,
sanding, grinding, serrating, diamond-cutting, coating with a
biocompatible and osteoconductive material (such as hydroxyapatite
(HA), tricalcium phosphate (TCP), or calcium carbonate), or coating
with osteoinductive materials (such as proteins from the
transforming growth factor (TGF) beta superfamily or
bone-morphogenic proteins, such as BMP2 or BMP7)), other structural
fixation devices (e.g., staple, nail, etc.), and/or combinations
thereof. Further, in some instances the bone screw incorporates one
or more biologic materials. As shown, the body portion 434 also
contains a sensor housing 436 therein. The sensor housing 436
contains all of the electronics and associated elements of the
sensor. Depending on the type of sensor utilized the housing 436
contain different elements. While the sensor housing 436 is shown
as being positioned substantially centrally within the body portion
436, in other embodiments the sensor is positioned off-center,
adjacent an end or surface of the body, and/or within the head
portion of the bone screw 430. The illustrated position of the
housing 436 is for exemplary purposes only and should not be
considered limiting.
The bone screw 430 can be utilized to create a model of the
patient's anatomy. In that regard, in some embodiments the bone
screw 430 is identified in imaging studies in relation to
anatomical features of the patient. In some embodiments, the size
of the bone screw 430 is well defined such that the relative size
of the bone screw to the anatomical features is utilized in
creating the model of the anatomical features. To that end, the
bone screw 430 has a length 438 extending between a proximal end
440 and a distal end 442. Further, the head portion 432 of the bone
screw has a height or thickness 444 extending between its uppermost
portion and its lowermost portion. The body portion 434 of the bone
screw 430 has a height or thickness 446 as measured from the outer
portion of the bone screw threads. In other embodiments, such as a
nail embodiment, the body portion 434 has a substantially constant
height 446. In the current embodiment, the height 444 of the head
portion 432 is larger than the height 446 of the body portion 434.
In other embodiments, the height of the head portion is
substantially equal to or less than the height of the body
portion.
The head portion 432 is configured for engagement with a driving
tool such that the driving tool may be utilized to secure the bone
screw 430 into a bone. In the current embodiment, a majority of the
head portion 432 is configured for engagement with a hex-shaped
driver. Accordingly, the bone screw 430 may be secured into the
bone by rotatingly driving the body portion 434 into the bone with
a hex-shaped driver (not shown). As best seen in FIG. 26, the head
portion 432 also includes a portion 448 that provides the bone
screw 430 with an asymmetric profile. That is, the portion 448
provides the bone screw 430 with a distinguishing feature such that
the orientation of the bone screw can be determined when viewed in
a image. Accordingly, the bone screw is not asymmetric in all
embodiments. Rather, in some embodiments the bone screw comprises a
substantially symmetrical profile, but includes one or more
features that allow the orientation of the bone screw to be
determined. Referring to FIG. 27, shown therein is a bone screw 449
according to another aspect of the present invention. The bone
screw 449 is substantially similar to bone screw 430 in many
respects, however, the bone screw 449 includes a head portion 450
illustrating an alternate profile. In particular, the head portion
450 includes a majority portion 451 having a substantially circular
profile and a minority portion 452 having a substantially planar
profile. Accordingly, the orientation of the head portion 450 and,
in turn, the bone screw 449 is determined by the position of the
minority portion 452 relative to the majority portion 451.
Generally, engaging the implantable sensors with bone provides a
fixed orientation between the sensor and the bone, which allows a
good correlation between the position of the sensor and the
position of the bone. In other embodiments, the sensors are
configured for engagement with softer tissues. In such embodiments,
the sensors include features to prevent unwanted movement of the
sensors relative to the tissue. Where the sensors are
implanted--temporarily or permanently--inside the body, the sensors
are introduced via a guidewire, needle, catheter, tube, and/or
other suitable implantation means. Preferably, the sensors are
implanted using a minimally invasive procedure and in some
instances are implanted percutaneously.
One or more sensors are placed adjacent to or within each
anatomical feature of interest. In some embodiments the sensors are
positioned adjacent to one or more of the following anatomical
features or parts thereof: heels, ankles, knees, hips, iliac
crests, sacrum, pelvis, spinal column, spinal column regions,
vertebrae, transverse processes, spinal processes, clavicles, and
other anatomical features. In one particular embodiment, the
sensors are placed on a portion of a plurality of vertebrae. In one
specific embodiment, the sensors are placed on the spinous
processes of at least two adjacent vertebrae. The actual anatomical
features for which sensors are located adjacent to depends on
numerous factors including physician preference, patient condition,
treatment plans, surgical procedures, and other factors. In some
embodiments, the anatomical feature(s) of interest are selected by
the treating physician or technician.
After the sensors have been introduced at step 422, the method 420
continues at step 424 in which an imaging technique is utilized to
obtain an image of the patient with the sensors attached to the
pertinent anatomical features. In orthopedic applications, the
imaging focuses on the relevant skeletal structures of the patient
to which the bone screw 430 or other sensor has been attached.
Generally speaking, the imaging of step 424 may include x-ray,
fluoroscopy, and/or CT scans. X-ray machines may be utilized to
obtain snap-shot images of the patient's skeletal structure.
Fluoroscopy machines may be utilized to obtain real-time images of
the patient's skeletal structure. In some embodiments, the imaging
step 424 is utilized to obtain images of the patient's spinal
column, pelvis, iliac crest, sacrum, hips, shoulders, clavicles,
skull, arms, legs, knees, ankles, feet, and/or combinations
thereof. In some embodiments, the imaging technique is utilized to
obtain at least sagittal and frontal images of the patient's
anatomy. In addition to the sagittal and frontal views, other views
of the patient's anatomy that are advantageous to patient analysis
are obtained in some instances.
The method 420 also includes step 426 in which the relative
positions of the sensors is determined. In that regard, the
relative positions of the sensors are determined with respect to
the anatomical features of the patient and/or the other sensors.
Generally, the implanted sensors include features that allow them
to be visualized on the images obtained using the imaging
technique. In some instances, the sensors or the housing of the
sensors (such as bone screw 430) are substantially radiopaque so as
to be visible on x-ray and/or fluoroscopy imaging. In that regard,
in some embodiments the sensors or the housing of the sensors (such
as bone screw 430) include features, such as asymmetric profiles or
otherwise, that allow the orientation of the sensor/housing
relative to the anatomical features to be determined from the
images.
From the images a 3-D or 2-D model of the patient's anatomy can be
created. In some embodiments, the model is animated to illustrate
and/or track a motion sequence of the patient's anatomy. In some
embodiments, the model can be updated in approximately real-time
based on the position of the sensors and/or accelerometer
information provided by the sensors to provide the surgeon or other
medical personnel with the relative locations of the anatomical
features with respect to one another. In some embodiments, the
model utilizes the relative motion between the implanted sensors to
monitor and update anatomical positioning. The movement of each
sensor with respect to the other sensors is tracked and utilized to
determine the relative motion between the anatomical features
associated with each sensor. The relative positions of the
anatomical features is determined therefrom. In some embodiments,
the model utilizes the absolute positions of the sensors to
correlate to the position of the anatomical features. That is, the
positions of the sensors are tracked with respect to one or more
reference points (e.g., a signal receivers), which can in turn be
utilized to determine the position of the anatomical features. In
some embodiments, the positions of the sensors are monitored using
wireless telemetry to measure the distances between each sensor.
For example, in some instances each sensor is registered with the
one or more signal receivers and the position of the sensor is
tracked using wireless telemetry. Based on the communication of the
sensor with the signal receiver a time of flight calculation can be
made to triangulate the position of the sensor with respect to the
signal receiver. Triangulation can be done either by lateration
(i.e., determining distance measurements to the sensors from the
receivers) or by angulation (i.e., determining angles between the
sensors and the receivers and computing the location of the sensors
based on the fixed dimensions between the receivers).
The method 420 continues with step 428 in which a treatment is
performed utilizing positional data provided by the sensors. In
that regard, a detailed treatment plan may have been established
and modeled as described above with respect to other embodiments.
Accordingly, the implanted sensors and resulting data may be
utilized in step 428 to ensure compliance with the planned
treatment and/or ensure that the treatment is performed within a
predetermined error field.
Referring more particularly to FIG. 28, shown therein is a system
460 illustrating step 428 of method 420 according to one particular
embodiment of the present disclosure. In that regard, the system
460 shows an upper vertebra 462, a lower vertebra 464, and an
intervertebral disc space 466. A bone screw 430, including sensor
436 therein, has been secured to each of the upper and lower
vertebrae 462, 464. In the illustrated embodiment, the natural disc
has been removed such that the intervertebral disc space 466 and
the vertebrae 462, 464 are configured to receive an artificial disc
prosthesis 468. In the illustrated embodiment, the prosthesis 468
comprises an upper portion 470 configured for engaging with the
upper vertebra 462 and a lower portion 472 configured for engaging
with the lower vertebra 464. The upper portion 470 articulatingly
engages the lower portion 472. Each of the upper and lower portions
470, 472 include sensors 474 therein. In the illustrated
embodiment, the sensors are positioned adjacent the anterior and
posterior portions of the prosthesis 468. These positions are for
exemplary purposes only and should not be considered limiting. In
particular, it is contemplated that one or more sensors 474 may be
positioned within and/or attached to the disc prosthesis 468. The
one or more sensors 474 are utilized to track the placement of the
disc prosthesis within the intervertebral disc space as it is
implanted and, in some embodiments, after implantation. In that
regard, the position of the sensors 474 are compared to the
positions of the sensors 436 to determine the relative position of
the prosthesis 468 within the disc space 466 in some embodiments.
In that regard, in some embodiments the sensors 474 communicate
directly with the sensors 436 to determine relative position of the
prosthesis 468. In other embodiments, a centralized receiver or
imaging device determines the position of the sensors 436 and 474
to determine the relative position of the prosthesis 468. Where the
prosthesis 468 includes one or more sensors 436 the tool utilized
for inserting the prosthesis need not necessarily have sensors
because the position of the prosthesis can be determined from the
sensors therein. However, in the illustrated embodiment the system
460 includes an insertion tool 476 having a plurality of sensors
478.
Similar to the sensors 474 within the prosthesis 468, the sensors
478 of the insertion tool 476 are utilized to track the placement
of the disc prosthesis within the intervertebral disc space 466 as
it is implanted. To ensure proper orientation between the insertion
tool 476 and the prosthesis 468 to allow a correlation between the
position of the tool and the position of the prosthesis, the
prosthesis 468 includes apertures (not shown) for receiving an
engagement portion of the insertion tool in some embodiments. The
position of the sensors 478 are compared to the positions of the
sensors 436 to determine the relative position of the prosthesis
468 within the disc space 466 in some embodiments. In that regard,
in some embodiments the sensors 478 communicate directly with the
sensors 436 to determine relative position of the prosthesis 468.
In other embodiments, a centralized receiver or imaging device
determines the position of the sensors 436 and 478 to determine the
relative position of the prosthesis 468. In some embodiments, both
sets of sensors 474 and 478 within the prosthesis and the insertion
tool 476 are utilized to monitor positioning of the prosthesis
468.
In some embodiments, the model of the patient's anatomical features
is updated in approximately real-time to illustrate the position of
the prosthesis 468 and/or insertion tool 476 relative to the
vertebrae 462, 464 and the sensors 436. In some embodiments, an
image guided surgery system utilizes the positional data from the
sensors 436, 474, and/or 478 to ensure proper placement and
orientation of the prosthesis within the disc space 466. In that
regard, in some embodiments the insertion tool 476 is part of the
image guided surgery system. Further, in some embodiments the image
guided surgery system is communication with the model and/or the
model is a component of the image guided surgery system such that a
visualization of the prosthesis and associated anatomical features
of the patient is provided to confirm proper placement of the
prosthesis within the disc space. In some embodiments, the image
guided surgery system utilizes the model in monitoring the
placement of the prosthesis. It is understood that the procedure
described above is exemplary and that numerous other treatment
procedures may be performed using the concepts described.
Referring now to FIG. 29, shown therein is a method 500 for
selecting and modifying implant parameters using implanted sensors
according to one embodiment of the present disclosure. The method
500 begins with step 502 in which one or more sensors are
introduced. In that regard, the particular type of sensors that are
introduced will depend on the patient anatomy to be monitored. In
some embodiments, the pertinent anatomical features of the patient
comprise a joint. In such embodiments, accelerometers and/or
gyroscopes are utilized as the sensors. Use of the accelerometers
and/or gyroscopes allows the sensors to track the motion of the
joint and thereby monitor the performance of the joint and any
implants or other medical treatments associated therewith.
Generally, the sensors are placed in close proximity to an
anatomical structure of interest. In that regard, in some instances
the sensors are placed on an implant, prosthesis, fixation device,
or other device that is part of a treatment plan. In other
instances, the sensors are stand alone units placed adjacent to the
anatomical structure of interest and any associated devices if
present.
In this manner, the sensors are utilized to correlate the position
of the anatomical structure based on the position of the sensor. In
some instances, a plurality of sensors are utilized adjacent to a
single anatomical feature to provide more accurate position data
for the anatomical structure. Depending on the anatomical features
of interest, the sensors may be implanted into the patient's body
adjacent to the anatomical feature of interest, placed on the skin
of the patient adjacent to the anatomical feature(s) of interest,
and/or placed on clothing of the patient adjacent to the anatomical
feature(s). In some embodiments, implantable sensors are preferred.
In some instances, sensors are used both inside and outside of the
patient's body. Implantable sensors facilitate direct contact with
the anatomical feature(s) of interest or at least provide
substantially closer placement to the anatomical features than
sensors that remain outside the patient's body. In that regard,
implantable sensors facilitate the accurate detection of the
position of internal anatomical features that cannot be accurately
determined with external sensors alone. The remaining description
of the present method 500 will be described with respect to
implanted sensors, however, no limitation is intended thereby.
In some embodiments, the implantable sensors are configured for
engagement with bone. In that regard, the implantable sensors are
part of a bone screw or other bone fixation device in some
embodiments. In other embodiments, the implantable sensors are
secured to the bone via a biocompatible adhesive or epoxy, a
structural fixation device (screw, staple, etc.), combinations
thereof, and/or other otherwise secured to the bone. Generally,
engaging the implantable sensors with bone provides a fixed
orientation between the sensor and the bone, which allows a good
correlation between the position of the sensor and the position of
the bone. In other embodiments, the sensors are configured for
engagement with softer tissues. In such embodiments, the sensors
include features to prevent unwanted movement of the sensors
relative to the tissue. The sensors are introduced via a guidewire,
needle, catheter, tube, and/or other suitable implantation means.
Preferably, the sensors are implanted using a minimally invasive
procedure and in some instances are implanted percutaneously. In
other embodiments, the sensors are implanted as part of a larger
surgical procedure and, therefore, are implanted through
non-minimally invasive means.
The sensors are utilized for tracking the position of one or more
anatomical features. In that regard, one or more sensors are placed
adjacent each anatomical feature of interest. In some embodiments
the sensors are configured for identifying the location and
tracking the motion of one or more of the following anatomical
features or parts thereof: heels, ankles, knees, hips, iliac
crests, sacrum, pelvis, spinal column, spinal column regions,
vertebrae, transverse processes, spinal processes, clavicles, and
other anatomical features. In one particular embodiment, the
sensors are placed on a plurality of vertebrae along the spine. The
relative motion of the sensors placed on each of the plurality of
vertebrae are utilized to obtain relative orientation and motion
information for the sensors, which in turn can be extrapolated to
the vertebrae. The actual anatomical features for which sensors are
located adjacent to depends on numerous factors including physician
preference, patient condition, treatment plans, surgical
procedures, and other factors. In some embodiments, the anatomical
feature(s) of interest are selected by the treating physician or
technician.
After the sensors have been introduced at step 502, the method 500
continues at step 504 in which the motion profile of the anatomical
features is tracked or measured. In some embodiments, the motion
profile is tracked by creating a model of the patient's anatomy and
simulating the patient's motion profile based on the sensor data.
In that regard, an imaging step is performed in some embodiments as
part of creating the model. In orthopedic applications, the imaging
focuses on the relevant skeletal structures of the patient, which
are typically the anatomical features of interest as well.
Generally speaking, the imaging may include x-ray, fluoroscopy,
and/or CT scans. X-ray machines may be utilized to obtain snap-shot
images of the patient's skeletal structure. Fluoroscopy machines
may be utilized to obtain real-time images of the patient's
skeletal structure, which may be beneficial in correlating the
model to the motion profile in some instances.
Generally, the data from the imaging protocol is utilized to create
the model. In one particular embodiment, the data from the imaging
protocol is utilized to segment the model into the individual
anatomical features of the patient. In that regard, a motion joint
is modeled by the combination of individual bones that come
together to form the joint. In some embodiments, the dimensions of
the implanted sensor are known and utilized to correlate bone
position to the sensor position. Further, the orientation of the
sensor to the bone is established by an asymmetry in the structure
of the sensor that is identifiable through the imaging protocol.
Accordingly, in some embodiments the known dimensions and features
of the implanted sensors are utilized in creating the model of the
patient's anatomical features. The model is either a 3-D or 2-D
representation of the patient's anatomy. In some embodiments, the
model is animated to illustrate the motion profile of the patient's
anatomy. In other embodiments, the model is simply a statistical
representation of the patient's anatomy and does not provide a
visualization. In that regard, a computer system is utilized to
analyze the patient's motion sequence and associated data to
provide suggested implant parameters and modifications thereto.
In some embodiments, the motion profile of the patient's anatomical
features is determined by putting the patient through a diagnostic
protocol. In that regard, the diagnostic protocol is a series of
movements that the patient is put through that utilizes the
anatomical features of interest. In some embodiments, the
diagnostic protocol is performed to measure joint motion and/or
relative motion between the anatomical features. Accordingly, the
diagnostic protocol often comprises a natural movement such as
walking, sitting, standing, lying down, or other common movements.
However, in other embodiments the diagnostic protocol comprises a
specific series of movements that include at least some movements
that are not performed on a regular basis. The precise movements or
structure of the diagnostic protocol depends on the anatomical
features of interest and/or the treating physician's
preference.
The implanted sensors are utilized to track the motion profile of
the patient's anatomy through the diagnostic protocol. In some
embodiments, the relative motion between the implanted sensors are
utilized to monitor the patient's motion profile. That is, the
movement of each sensor with respect to the other sensors is
tracked and utilized to determine the relative motion between the
anatomical features associated with each sensor. In some
embodiments, the absolute positions of the sensors are tracked and
correlated to the motion of the anatomical features. That is, the
positions of the sensors are tracked with respect to a reference
point(s) (e.g., signal receiver(s)), which can in turn be utilized
to determine the motion of the anatomical features. In some
embodiments, the positions of the sensors are monitored using
wireless telemetry to measure the distances between each sensor.
For example, in some instances each sensor is registered with one
or more signal receivers and the position of the sensor is tracked
using wireless telemetry. Based on the communication of the sensor
with the signal receiver a time of flight calculation can be made
to triangulate the position of the sensor with respect to the
signal receivers over time. The positions of each of the sensors
can then be compiled to identify the relative motion sequence of
the anatomical features with respect to one another. Taken
together, the motion sequence of the anatomical features of
interest is established.
In other embodiments, an electromagnetic measurement system is
utilized to track the positions of the implantable sensors during
the diagnostic protocol. For example, the electromagnetic
measurement system can detect the presence of sensors excitable by
an electromagnetic field to determine the position of the
anatomical features associated with the sensor. The electromagnetic
measurement system utilizes a computer system to calculate the 3-D
position of the anatomical feature(s) based on the position of the
sensors. In some embodiments, the electromagnetic measurement
system is configured to detect the position of sensors in a fixed
volume of space. In that regard, in some embodiments the fixed
volume of the electromagnetic measurement system is sufficient to
obtain the position of all relevant anatomical features of a
patient. In other embodiments, however, the fixed volume may be
sufficient to obtain 3-D positions of only some anatomical features
of a patient. Where the fixed volume is sufficient to obtain 3-D
positions of some, but not all of the patient's anatomical
features, a portion of the electromagnetic measure system is
moveable such that the 3-D positions of all the anatomical features
of most interest can be obtained.
In some instances, the sensor systems of the present disclosure are
self-calibrating. In that regard, the relative orientation and/or
position of the sensors is determined by the sensors and associated
components without need of manual input from a user or medical
personnel. For example, in one embodiment, the implantable sensors
interface with a software suite for tracking the positioning of the
sensors. Each of the sensors provides an initial coordinate
position. In some instances the initial coordinate position will be
an arbitrary coordinate. In other instances, the initial coordinate
position will be relative to a known point of reference (e.g., a
main sensor, a reference point in the room, an anatomical reference
point of the patient, or otherwise). Based on the initial
coordinate position the software suite will reset or zero out the
location of each of the sensors to this starting point.
Accordingly, subsequent movements can be compared to this initial
starting position.
After the motion profile of the anatomical features has been
tracked at step 504, the method 500 continues at step 506 in which
the motion profile is analyzed. In some embodiments, the motion
profile is analyzed by updating the model and/or creating a model
to simulate the detected motion profile. As described above the
model is a 3-D and/or 2-D animated model of the patient's anatomy
for visualizing the patient's anatomy in some instances. In other
instances, the model is simply a numerical or statistical
representation of the patient's motion profile that is utilized by
a computer system to analyze the patient's anatomical motion
profile. In some embodiments, the animated model is used to
highlight the problem areas and/or times in the patient's
anatomical motion sequence. In that regard, the model includes
layers of anatomical features that are selectively included or
removed. For example, in one embodiment the patient's motion
anatomy is grouped into layers according to types of anatomical
tissue, such as bones, cartilage, ligaments, tendons, muscles,
and/or combinations thereof. The animated model then simulates the
motion according to each grouping of anatomical tissue and the
interactions therebetween.
The motion sequence of the patient is analyzed by a computer system
and/or medical personnel. In some embodiments, the model itself
includes features to allow medical personnel and/or a computer
system to analyze the patient's motion sequence. In other
embodiments, a separate software suite or program is utilized to
analyze the patient's motion sequence. In that regard, in some
embodiments the model includes a stress grid overlay that indicates
potential areas of increased stress or strain on the patient's
anatomy, such increased muscle activity; overstretching of muscles,
ligaments, and/or tendons; friction between bones; and/or other
areas of stress/strain. In some embodiments the model allows for
zooming, panning, or otherwise changing the orientation of the view
of the patient's anatomy. A user adjusts the orientation to better
observe or isolate potential problem areas. Similarly, the animated
model allows a user to pause, rewind, slow down, and/or speed up
simulation of a motion sequence to better observe a potential
problem. Further, the model allows 3-D and/or 2D tracking of
specific anatomical features through the motion sequences. In some
embodiments, the animated model highlights potential problem areas
automatically based on a comparison to a standardized model. For
example, the system identifies anatomical features with a motion
sequence outside of a predetermined range in some instances. In
that regard, the standardized model and/or predetermined range of
normal motion are at least partially defined by a general patient
population.
In some embodiments, the treating physician or medical personnel
highlights potential problem areas based on their observations of
the patient's motion sequence. In some embodiments, the problem
areas are identified by a computer system and/or medical personnel
by recognizing an abnormal motion pattern(s). In some instances,
the abnormal motion patterns are grouped into motion signatures
that are indicative of a specific type or grouping of medical
conditions. Each of the motion signatures, in turn, is associated
with appropriate medical treatment options for correcting the
medical condition(s) associated with the motion signature.
Based on the analysis of the patient's motion profile at step 506,
the method 500 continues at step 508 in which the treatment
parameters are modified or defined in an effort to correct any
problems in the motion profile. In some embodiments, the treatment
parameters comprise the placement, orientation, stiffness, and/or
other aspects of an implant. In that regard, in some embodiments
the diagnostic protocol is performed during the surgical procedure
such that the implant parameters are modified without need for a
subsequent medical procedure. For example, in one embodiment the
method 500 is utilized to balance a knee arthroplasty. In other
embodiments, the diagnostic protocol is performed post-surgery in
an effort to maintain and/or improve the effectiveness of the
treatment. In that regard, when modifications to the implant
parameters are suggested, a revision surgery may be required.
However, in some embodiments, the implant includes features that
allow non-invasive adjustment of the implant. For example, in some
embodiments the implant includes one or more actuators to adjust
the position of the implant relative to a fixation device. In other
embodiments, the implant includes one or more actuators to adjust
the relative stiffness of the implant. In some embodiments, the
implanted sensors are utilized with a dynamic fixation system such
as that described in U.S. patent application Ser. No. 11/356,687
filed Feb. 17, 2006 and titled "Sensor and Method for Spinal
Monitoring," herein incorporated by reference in its entirety. In
that regard, in such embodiments the dynamic actuators that control
the dampening force of the implant are adjusted based on the
parameters as indicated by the feedback of the sensors. The sensors
are utilized to adjust other adjustable implants. In other
embodiments, the treatment plan does not include an implant and,
therefore, the modified parameters are not related to the
implant.
After the treatment parameters have been modified and/or defined in
step 508, the method 500 returns to step 504 where the motion
profile is again monitored and then analyzed at step 506. If the
analysis again detects problems in the motion profile, then the
method returns to step 508 for additional modification of the
treatment parameters. Accordingly, steps 504, 506, and 508 are
iterated until a satisfactory motion profile is established with
selected the treatment parameters. When the treatment parameters
have been defined to achieve the desired motion profile, then the
method 500 concludes with step 510 in which the treatment
parameters are finalized. The finalized treatment parameters are
then implemented. In some embodiments, steps 504, 506, and 508 are
repeated at various intervals after an initial treatment to
maintain the desired motion profile of the patient's anatomy.
In some embodiments, an implant, prosthesis, fixation element, or
other device is instrumented with a plurality of sensing elements
to monitor various conditions within a patient. For example,
referring more specifically to FIGS. 30 and 31 shown therein is a
device 520 according to one aspect of the present disclosure. In
the illustrated embodiment, the device 520 comprises a bone anchor
or screw configured for engagement with a bony structure of a
patient. This illustrated embodiment is merely for exemplary
purposes and numerous other types of implants may be similarly
fitted with multiple sensors in other embodiments. Generally, the
device 520 includes a head portion 522 and a body portion 524. The
head portion 522 is configured for engagement with an insertion
instrument and, in some embodiments, has a substantially hex-shaped
profiled for mating with a hex-shaped driver. In other embodiments,
the head portion 522 has other profiles and/or includes recessed
portions for engaging with an insertion instrument or driver. The
body portion 524 comprises a series of threads for engaging with a
bone. The device 520 also includes a opening 526 for housing the
sensors extending along its length from a proximal portion 528 to a
distal portion 530. In other embodiments, the opening 526 may
extend along only a portion of the device 520. For example, in some
embodiments the opening is completely contained within the body
portion 524 of the device 520. In some embodiments, the device does
not include a single opening for housing the sensors, but contains
multiple openings for housing the sensors. In some embodiments, the
sensors are positioned on the outer surfaces of the device. In some
instances an implant is instrumented or fitted with various sensors
capable of detecting physical parameters.
A plurality of sensors are positioned within the opening 526. In
the illustrated embodiment, a chemical sensor 532, a multi-purpose
sensor 534, an accelerometer 536, and a pressure sensor 538 are
included within the opening 526. In the current embodiment, if the
device 520 was secured to a vertebra the sensors 532, 534, 536, and
538 could be utilized to monitor vertebral motion, load/stress,
and/or the existence or quantity of particular proteins adjacent to
the site. In that regard, the position, order, or orientation of
the sensors with respect to the device 520 may also affect the
parameters that are monitored. For example, in some embodiments the
pressure sensor is positioned substantially in the head portion 522
of the device. In such embodiments, the pressure sensor is utilized
to monitor the patient's heart rate, swelling, and/or other
pressure related parameters external to the vertebral joint.
Accordingly, the placement and orientation of the sensors relative
to one another, the device, and/or anatomical features is selected
based on the parameters to be monitored.
Generally, the sensors 532, 534, 536, and 538 are selected from
sensors that are able to monitor various physical parameters
associated with the patient's anatomical features and/or treatment
device, such as pressure, linear displacement, angular
displacement, torque, velocity, acceleration, temperature, or pH.
In some instances the sensor may be a multi-purpose sensor in that
it can be programmed or modified to monitor various parameters. A
pressure sensor may, for example, use Wheatstone bridge based
strain gauge technology. Alternative pressure sensors may include
inductive or capacitive measurement systems. A linear displacement
sensor may, for example, use linear variable differential
transformer (LVDT) technology to measure linear displacements.
Likewise, an angular displacement may, for example, use rotational
variable differential transformer (RVDT) technology to measure
angular displacement. An acceleration sensor may, for example,
include an accelerometer. It is understood that multiple sensors of
various types may be used in a single implant to measure different
physical parameters. The particular types of sensors to be included
within the device 520 depends on the selected treatment, the
anatomical feature(s) being treated, physician preference, and/or
other factors. In some instances, the device is fully assembled
with a predetermined collection of sensors pre-operatively. In
other instances, the device is modular such that sensors having the
desired parameters are selected from a kit comprised of a plurality
of sensors and are inserted into the device pre-operatively or
intra-operatively.
In some embodiments, the sensors are utilized to measure anatomical
and/or physiological data that is then transferred externally.
Accordingly, in embodiments where multiple sensors are utilized it
can be necessary to distinguish between the various sensors. In one
embodiment, the communication frequencies of the sensors are
differentiated to decrease airwave clutter and/or prevent data from
being mixed up. In some embodiments, the sensors are coded to
automatically establish sensor-to-sensor relationships.
Accordingly, a network of sensors can be created by the
sensor-to-sensor relationships. The sensor-to-sensor communications
are utilized to increase efficiency and/or improve processing times
in some instances. In some embodiments each sensor includes a
unique identification that is associated with it. In some
embodiments, each sensor is assigned a unique serial number or
identification number. The serial number or identification number
is then transferred along to the external device with any data.
Accordingly, any data received from the sensors is associated with
a particular sensor. This allows for easy association of the
received data to the sensors by checking the serial number
accompanying the data.
Further, in some instance the serial number of each sensor is
further associated with the patient. Accordingly, even the raw data
received from the sensors can be associated with the correct
patient. In that regard, the serial number is used to access
patient data in some embodiments. For example, patient data such as
prior diagnoses, prior treatments, height, weight, blood pressure,
etc. can be provided to the medical personnel treating the patient.
In other instances, previous diagnostic studies and/or data sets
from the sensors can be provided to the medical personnel treating
the patient. In that regard, the prior data sets are compared to
the current data sets in some instances. In some instances, the
serial number is associated with a patient, but all private
information regarding the patient is disassociated from the serial
number. For example, the serial number may be associated with the
general characteristics of the patient (such as age, height,
weight, medical condition, treatment plan, etc.), but private
information (such as the patient's name, address, social security
number, etc.) are not associated with the serial number.
Accordingly, the sensors are utilized in some embodiments for
providing data without any personal information to a database for
later use. Such a system could be utilized to streamline referrals,
reduce costs, and/or generally improve patient care. In some
embodiments, the sensors are utilized in an ER or other situation
where the treating medical personnel knows nothing or very little
about the patient and the patient's primary care physician is
unavailable. The data associated with the sensor can be utilized to
provide additional information to the medical personnel that may be
crucial in determining the appropriate treatment options for the
patient in the emergency.
Generally, the data collected from the various sensors is used to
monitor the effectiveness (or lack thereof) of the treatment,
modify the treatment plan, monitor the position of an implanted
device, and/or otherwise monitor the anatomical area of interest.
The data from the sensors can be stored in a database for analysis
and consideration in later patient treatments. In some instances
the data is utilized to refine the design of the device or implant.
For example, understanding forces exerted on a device and the
resulting pressure concentrations within the device may permit
design changes that can reduce the weight of the implant and/or
localize material strength though material selection or material
thickness.
In some instances, one or more of the sensors positioned within the
device 520 are selectively activated and de-activated. In some
embodiments, the device 520 is reprogrammable such that the active
sensors can be changed or modified. In that regard, allowing the
device 520 to be reprogrammed multiple times can extend sensor
life, improve data exchange efficiency, and/or minimize power
consumption.
Referring now to FIG. 32, shown therein is a system 540 for
monitoring implant loosening according to one embodiment of the
present disclosure. A vertebra 542 has been engaged by a bone
fixation device 544, as shown. A sensor 546 is positioned within or
on the bone fixation device 544. In some instances, the bone
fixation device 544 is part of a larger treatment system (not
shown). For example, in some instances the bone fixation device 544
is part of a rod and screw system for limiting the motion of
vertebrae. In other embodiments, the bone fixation device 544 is
utilized as part of a dynamic fixation system. A sensor 548 is
positioned adjacent to the bone fixation device 544 and the sensor
546. In some embodiments, the sensor 548 is fixed with respect to
the vertebra 542. In that regard, the sensor 548 may itself engage
the bone and/or the sensor 548 may be attached or imbedded within a
housing that engages to the bone. The sensors 546 and 548 are
utilized to monitor and/or detect any loosening of the bone
fixation device 544 relative to the vertebra 542.
In that regard, the sensors 546 and 548 are accelerometers in some
embodiments. The relative motion of the sensors 546 and 548 with
respect to one another is detected. If the motion patterns of the
sensors 546 and 548 are substantially similar, then the bone
fixation device 544 is substantially fixed with respect to the
vertebra. This is because the sensor 546 is fixed with respect to
the bone fixation device and the sensor 548 is fixed with respect
to the vertebra 542. However, if the motion patterns of the sensors
546 and 548 are divergent, then this can be an indication of
loosening of the bone fixation device 544 relative to the vertebra.
In that regard, the magnitude of the divergence between the motion
patterns of the sensors 546 and 548 can be indicative of the amount
or degree of loosening of the bone fixation device 544. In other
embodiments, the relative angles of the sensors with respect to one
another is monitored. If the fixation device 544 remains
substantially fixed to the vertebra 542, then relative angles of
the sensors 546 and 548 remains substantially fixed as well.
However, if the fixation device 544 has loosened, then the fixation
device may toggle with respect to the vertebra 542 and the relative
angles of the sensors 546 and 548 will change. Accordingly, the
relative angles of the sensors 546 and 548 are utilized in some
embodiments to detect loosening. In some instances, the degree of
loosening is monitored over time. The loosening information may be
utilized to determine the need for additional treatment and/or
revision surgery to correct the loosening. In some embodiments,
more than one sensor is fixed relative to the vertebra 542 and/or
the fixation device 544. Use of multiple sensors can prevent a
false detection of loosening where the sensor itself has become
loose for some reason.
While the detection of loosening has been described with respect to
a bone fixation device, similar concepts are utilized for
monitoring loosening of other implants, including other fixation
devices, prosthetic device, and/or sensors. Further, the detection
of loosening is not limited to the spinal region, but is utilized
throughout the body where implants are fixed with respect to
anatomical features. For example, referring now to FIG. 33, shown
therein is a system 550 for monitoring implant loosening according
to another embodiment of the present disclosure. In particular, the
system 550 illustrates a long bone 552 that has received an implant
554, as shown. In some instances, the implant 554 comprises an
intramedullary rod or nail. The implant 554 includes a sensor 556
therein. The bone 552 includes a pair of sensors 558 and 560. As
discussed above, the use of multiple sensors--such as sensors 558
and 560--provides a redundancy that helps prevent false detection
of implant loosening. In other embodiments, the implant 554 also
includes multiple sensors. In some embodiments, the implant 554
includes a sensor proximal to each end of the implant. Generally,
the sensors 556, 558, and 560 are utilized to detect loosening of
the implant 554 with respect to the bone 552 in a similar manner as
described above.
In some instances, the implantable sensors and/or implants
including the sensors are in communication with a device or system
for remotely communicating data obtained by the sensors to a
medical facility or medical personnel. For example, in some
instances the implantable sensors and/or implants are configured
for communication with a system such as the CARELINK system from
Medtronic. In some instances, data from the sensors is transferred
to the medical facility or personnel at a regular interval (e.g.,
once a day, week, or otherwise). In other instances, data from the
sensors is transferred to the medical facility or personnel upon
the sensors sensing an abnormality or change in one or more of the
conditions monitored by the sensors. In some instances, the sensors
and/or implants are associated with reservoirs of pharmaceuticals
for controlled dispensing depending on the sensed conditions. Such
reservoirs are utilized for pain management, to encourage healing,
promote tissue growth, or otherwise in some instances. In some
instances, one or more devices or methods as described in U.S.
patent application Ser. No. 11/217,693 filed Sep. 8, 2006 entitled
"Controlled Release Systems and Methods for Osteal Growth," U.S.
patent application Ser. No. 11/517,771 filed Sep. 8, 2006 entitled
"Controlled Release Devices for Fusion of Osteal Structures,"
and/or U.S. patent application Ser. No. 11/410,216 filed Sep. 8,
2006 entitled "Controlled Release Systems and Methods for
Intervertebral Discs," each of which is incorporated by reference
herein in its entirety. In other instances, the implants are
adjustable based on the sensed conditions. For example, in some
instances the stiffness and/or dampening of an implant is adjusted
based on the sensed conditions. In some instances, devices as
described in U.S. patent application Ser. No. 12/048,627, filed
Mar. 14, 2008 entitled "Intervertebral Implant and Methods of
Implantation and Treatment," hereby incorporated by reference in
its entirety, are utilized. In some instances, devices similar to
those described in PCT/US2005/020116 filed Jun. 8, 2005 entitled
"Prosthetic Intervertebral Spinal Disc With Integral
Microprocessor," hereby incorporated by reference in its entirety,
are utilized.
In one embodiment, a method of patient assessment and outcome
modeling comprises: obtaining patient characteristic information
from a current patient; defining a plurality of therapeutic factors
based on the characteristic information of the current patient;
weighting the therapeutic factors; accessing at least one database
having medical records of prior patients, the medical records
including at least prior patient characteristic information, prior
patient treatment plan, and prior patient outcome; comparing the
weighted factors of the current patient to the medical records of
the prior patients to identify one or more relevant prior patient
records; retrieving at least a portion of the relevant prior
patient records, the portion including at least the prior patient
treatment plan and the prior patient outcome; and performing a
simulation of at least one of the prior patient treatment plans
based on the current patient's characteristic information.
In some instances, the method further comprises identifying at
least one available treatment plan for the current patient. In some
instances, the database includes information collected from one or
more treatment studies. In some instances, the steps of accessing
at least one database, comparing the weighted factors of the
current patient to the medical records of the prior patients,
retrieving at least a portion of the relevant prior patient
records, and performing the simulation are executed electronically.
In some instances, the steps of accessing at least one database,
comparing the weighted factors of the current patient to the
medical records of the prior patients, retrieving at least a
portion of the relevant prior patient records, and performing the
simulation are executed over a computer network. In some instances,
at least one of the steps of accessing at least one database,
comparing the weighted factors of the current patient to the
medical records of the prior patients, retrieving at least a
portion of the relevant prior patient records, and performing the
simulation is executed remotely over a computer network. In some
instances, at least two available treatment plans are identified
and further comprising ranking the at least two available treatment
plans. In some instances, the method further comprises performing a
simulation of the at least two available treatment plans, where the
ranking is at least partially based on the simulations. In some
instances, the ranking is at least partially based on the success
of the available treatment plans for the one or more relevant prior
patient records. In some instances, the prior treatments and the
administered treatments include a spinal surgical procedure. In
some instances, the patient characteristic information includes
patient characteristic information obtained from diagnostic
tests.
In one embodiment, a system for pathology assessment, treatment,
and outcome modeling comprises: a database having a plurality of
records of prior patients, the records including patient
characteristic information, treatment information, and outcome
information; and at least one processing system operatively
connected to the database, the at least one processing system
comprising a diagnosis module, a modeling module, and a treatment
module; where the diagnosis module is configured to receive and
weight current patient information, compare the current patient
information to the plurality of records of the database, and
retrieve records of prior patients with similar characteristic
information from the database, the treatment module is configured
to identify available treatment options for the current patient,
and the modeling module is configured to simulate the available
treatment options for the current patient, wherein the simulation
is at least partially based on the outcome information from the
records of prior patients. In some instances, the diagnosis module
is configured to monitor the outcome of a treatment of the current
patient. In some instances, the database is remote from at least
one of the diagnosis module, the modeling module, and the treatment
module.
In one embodiment, a method for patient assessment and outcome
prediction comprises: obtaining a plurality of therapeutic factors
from a current patient, said factors based at least partially on
the current patient's physical characteristics, pathology, and
desired therapeutic outcomes; weighting the therapeutic factors;
accessing at least one database having records of prior patient
treatments, including prior patient therapeutic factors, treatment
plans, and treatment outcomes; comparing the therapeutic factors of
the current patient with the prior patient therapeutic factors in
the records of the database to identify prior patients with similar
therapeutic factors; retrieving from the database at least a
portion of one or more records of prior patients with similar
therapeutic factors; identifying one or more available treatment
plans for the current patient based at least in part on the records
of the prior patients with similar therapeutic factors; and
predicting a likelihood of success for each of the one or more
available treatment plans for the current patient.
In some instances, the available treatment plans are identified
based on the success of the treatment plans with prior patients
with similar therapeutic factors. In some instances, method further
comprises simulating the one or more available treatment plans
based on the current patient's physical characteristics, pathology,
and desired therapeutic outcomes. In some instances, the method
further comprises selecting a treatment plan at least partially
based on the simulating of the one or more available treatment
plans. In some instances, selecting a treatment plan is at least
partially based on the treatment outcomes of the prior patients
with similar therapeutic factors. In some instances, accessing the
at least one database includes accessing the at least one database
from a remote location.
In one embodiment, a method for identifying available treatment
options for a patient having an increased likelihood of success,
comprising: obtaining a plurality of therapeutic factors from a
current patient, said factors based at least partially on the
current patient's physical characteristics, pathology, and desired
therapeutic outcomes; weighting the therapeutic factors; accessing
at least one database having records of prior patient treatments,
including prior patient therapeutic factors, treatment plans, and
treatment outcomes; comparing the therapeutic factors of the
current patient with the prior patient therapeutic factors in the
records of the database to identify prior patients with similar
therapeutic factors; retrieving from the database at least a
portion of one or more records of prior patients with similar
therapeutic factors; and identifying available treatment options
for the current patient based at least in part on the records of
the prior patients with similar therapeutic factors.
In some instances, the available treatment options are identified
based on the success of the treatment options with prior patients
with similar therapeutic factors. In some instances, the method
further comprises simulating the one or more available treatment
options based on the current patient's physical characteristics,
pathology, and desired therapeutic outcomes. In some instances, a
specific treatment option is selected from the one or more
available treatment options at least partially based on the
simulating of the one or more treatment plans. In some instances,
the specific treatment option is selected at least partially based
on the treatment outcomes of the prior patients with similar
therapeutic factors. In some instances, the accessing the at least
one database includes accessing from a remote location. In some
instances, accessing the at least one database is executed remotely
over a computer network. In some instances, the method further
comprises ranking the available treatment options. In some
instances, the ranking is at least partially based on simulating
the available treatment options. In some instances, the ranking is
at least partially based on the prior patient outcomes. In some
instances, the patient characteristic information includes patient
characteristic information obtained from diagnostic tests. In some
instances, the diagnostic tests include imaging.
In one embodiment, a system for identifying available treatment
options for a current patient having an increased likelihood of
success comprises: at least one local database having a plurality
of records of prior local patients, the records including patient
characteristic information, treatment information, and outcome
information; at least one remote database having a plurality of
records of prior remote patients, the records including patient
characteristic information, treatment information, and outcome
information; at least one processing system operatively connected
to the local and remote databases, the at least one processing
system comprising a diagnostic module, a modeling module, and a
treatment module; where the diagnostic module is configured to
receive and weight current patient information, compare the current
patient information to the plurality of records of in the local and
remote databases, and retrieve records of prior patients with
similar characteristic information from the local and remote
databases, the treatment module is configured to identify available
treatment options for the current patient based at least partially
on the records retrieved from the local and remote databases by the
diagnostic module, and the modeling module is configured to
simulate the available treatment options for the current patient
identified by the treatment module, wherein the simulation is at
least partially based on the outcome information from the records
of prior patients retrieved from the local and remote databases. In
some instances, the treatment information stored in the local and
remote databases includes medical products used in the treatment.
In some instances, the processing system is operatively connected
to the local database in order to store current patient information
in the local database. In some instances, the local database is at
least partially accessible by a remote processing system. In some
instances, private information stored in the local database is not
accessible by a remote processing system.
In one embodiment, a method for identifying available treatment
options comprises: accessing at least one database having records
of prior patients, including prior patient treatment plans and
treatment outcomes; identifying prior patients with similar
characteristics to a current patient; retrieving from the database
at least a portion of the records of prior patients with similar
characteristics to the current patient, the portion of the records
including the treatment plans and treatment outcomes; and
identifying successful treatment plans of prior patients based on
the treatment outcomes. In some instances, the method further
comprises modeling the successful treatment plans identified based
on the current patient's characteristics. In some instances, the
method further comprises ranking the successful treatment plans at
least partially based on the modeling.
In one embodiment, a method of obtaining and analyzing patient
information for diagnosis and treatment comprises: identifying at
least one patient symptom; selecting at least one patient category
associated with the at least one patient symptom; obtaining data
corresponding to the at least one patient category; providing the
obtained data to a software application; analyzing the obtained
data with the software application; and providing a summary of the
software application analysis for use in diagnosing the patient's
medical condition and identifying available treatment options.
In some instances, selecting the at least one patient category
comprises selecting a patient category from a predefined set of
patient categories. In some instances, each patient category of the
predefined set of patient categories includes an associated data
collection set that defines a plurality of data items corresponding
to the patient category. In some instances, obtaining data
corresponding to the at least one patient category comprises
performing a diagnostic test. In some instances, obtaining data
corresponding to the at least one patient category comprises asking
the patient a series of questions. In some instances, obtaining
data corresponding to the at least one patient category comprises
obtaining data from a previous medical exam. In some instances, the
data from the previous medical exam is provided by a referring
medical institution. In some instances, the method further
comprises comparing the summary to a prior patient data set to
identify previously successful treatment plans of prior patients in
a similar patient category. In some instances, the method further
comprises modeling the previously successful treatment plans based
on the at least one patient symptom. In some instances, the method
further comprises selecting a treatment plan for the current
patient based at least partially the comparison. In some instances,
the selected treatment plan is a previously successful treatment
plan of a prior patient in a similar patient category.
In one embodiment, a method of obtaining and analyzing patient
information for diagnosis and treatment comprises: submitting a
patient to diagnostic testing; obtaining results from the
diagnostic testing; categorizing the patient based on the results
from the diagnostic testing; obtaining additional data regarding
the patient, the data being associated with the categorization of
the patient; providing the obtained data and the results from the
diagnostic testing to a software application; analyzing the
obtained data and results from the diagnostic testing with the
software application; and identifying at least one available
treatment option for the patient based on the analysis.
In some instances, submitting the patient to diagnostic testing
includes imaging. In some instances, analyzing the data and results
comprises creating a model of a portion of the patient's anatomy.
In some instances, identifying at least one available treatment
option comprises simulating the at least one treatment option
within the model. In some instances, identifying at least one
available treatment option further comprises identifying successful
treatment options of previous patients with a similar
categorization. In some instances, categorizing the patient
comprises selecting a category from a predefined set of categories.
In some instances, categorizing the patient is performed by a
computer system based on the results of the diagnostic testing. In
some instances, obtaining additional data regarding the patient
comprises performing additional diagnostic tests. In some
instances, providing the obtained data and the results from the
diagnostic testing to the software application comprises sending
the data and results over a computer network.
In one embodiment, a method of visualizing and analyzing anatomical
motion comprises: providing a plurality of implantable sensors,
each sensor configured for implantation adjacent to an anatomical
feature of a patient; tracking the positions of the implantable
sensors as the patient is put through a diagnostic motion protocol;
correlating the positions of the implantable sensors to the
positions of the anatomical features of the patient adjacent to the
sensors; visualizing a motion sequence of the anatomical features
according to the positions of the anatomical features from the
diagnostic motion protocol; and analyzing the motion sequence of
the anatomical features to identify a medical problem.
In some instances, tracking the positions of the implantable
sensors comprises using wireless telemetry communication between
the sensors and at least one receiver. In some instances, the
positions of the implantable sensors are determined by
triangulation. In some instances, tracking the positions of the
implantable sensors comprises monitoring the relative movement
between the sensors. In some instances, the relative movement
between the sensors is monitored by comparing accelerometer data
from the sensors. In some instances, the method further comprises
securely attaching each of the implantable sensors to a portion of
the adjacent anatomical feature. In some instances, securely
attaching the implantable sensor comprises threadingly engaging a
housing of the sensor with a bone. In some instances, visualizing
the motion sequence of the anatomical features comprises creating
an animated model of the anatomical features. In some instances,
analyzing the motion sequence comprises comparing the animated
model to a standardized model to identify an abnormality in the
motion sequence. In some instances, the method further comprises
correlating the abnormality in the motion sequence to identify the
medical problem. In some instances, the method further comprises
imaging the patient with the sensors implanted. In some instances,
the method further comprises determining a relative orientation
between each of the implantable sensors and each of the adjacent
anatomical features based on the imaging. In some instances, the
method further comprises using the imaging and relative
orientations to create an initial model of the patient's anatomical
features. In some instances, the method further comprises updating
the model based on the positions of the anatomical features from
the diagnostic motion protocol.
In one embodiment, a system for visualizing and analyzing
anatomical motion comprises: a plurality of implantable sensors,
each sensor configured for implantation adjacent to an anatomical
feature of a patient; a monitoring system in communication with the
implantable sensors, the monitoring system configured to track the
positions of the sensors within the patient during a diagnostic
motion protocol; at least one processing system in communication
with the monitoring system, the at least one processing system
comprising a modeling module configured to create an animated model
of the patient's anatomical features based at least partially on
the positions of the sensors as tracked by the monitoring system
during the diagnostic motion protocol.
In some instances, each of the plurality of implantable sensors are
configured for engagement with a bone structure. In some instances,
the system further comprises an imaging device in communication
with the at least one processing system, wherein the animated model
is at least partially based on data obtained by the imaging device.
In some instances, each of the plurality of sensors comprises an
asymmetrical profile such that an orientation of the sensor with
respect to the adjacent anatomical feature is detectable by the
imaging device. In some instances, the monitoring system comprises
a wireless telemetry receiver system configured for communication
with the plurality of implantable sensors. In some instances, the
monitoring system comprises a plurality of receivers and is
configured to determine the positions of the sensors via
triangulation.
In one embodiment, a method of performing a surgical procedure
using implantable sensors comprises: providing one or more
implantable sensors, each sensor configured for implantation
adjacent to an anatomical feature of a patient; imaging the patient
to determine the relative positions of the one or more implantable
sensors relative to the anatomical features of the patient;
inserting an implant adjacent to at least one of the anatomical
features; and tracking the position of the implant relative to the
at least one anatomical feature during the inserting of the implant
using the implantable sensors.
In some instances, at least one of the anatomical features is a
vertebra. In some instances, at least one of the implantable
sensors comprises a housing having a bone engaging portion. In some
instances, at least one of the implantable sensors comprises an
asymmetrical profile such that an orientation of the sensor with
respect to the adjacent anatomical feature is detectable from the
imaging. In some instances, the implant includes a sensor therein
and wherein tracking the position of the implant comprises tracking
the relative position of the sensor within the implant to at least
one of the implantable sensors. In some instances, inserting the
implant comprises grasping the implant with a surgical tool. In
some instances, the surgical tool includes a sensor therein and
wherein tracking the position of the implant comprises tracking the
relative position of the sensor within the surgical tool to at
least one of the implantable sensors. In some instances, tracking
the position of the implant comprises visually monitoring the
insertion of the implant. In some instances, the method further
comprises monitoring the position of the implant relative to the at
least one anatomical feature using the implantable sensors after
insertion of the implant. In some instances, the implant comprises
a spinal implant. In some instances, the implant comprises an
artificial disc. In some instances, tracking the position of the
implant relative to the at least one anatomical feature comprises
tracking the relative position of a sensor associated with the
implant to at least one of the plurality of implantable sensors. In
some instances, the sensor associated with the implant is
positioned within the implant. In some instances, the sensor
associated with the implant is positioned in a surgical tool for
inserting the implant. In some instances, the implant is inserted
using an image-guided system.
In one embodiment, a method of inserting a spinal implant
comprises: providing at least one sensor, the at least one sensor
being positioned within a housing having a bone engaging portion
and an asymmetrical head portion; engaging the bone engaging
portion of the housing with a vertebra; imaging the patient to
determine the relative position of the sensor relative to the
vertebra using the asymmetrical head portion of the housing as a
guide; inserting an implant adjacent to the vertebra; and tracking
the position of the implant relative to the vertebra by correlating
the relative position of the implant to the sensor to the vertebra.
In some instances, the implant includes a sensor therein and
wherein tracking the position of the implant comprises tracking the
position of the sensor within the implant relative to the sensor
positioned within the housing. In some instances, inserting the
implant comprises using a surgical tool to guide the implant to a
position adjacent to the vertebra, the implant having a fixed
relationship with respect to the surgical tool when engaged with
the surgical tool. In some instances, the surgical tool includes a
sensor therein and tracking the position of the implant comprises
tracking the position of the sensor positioned within the surgical
tool relative to the sensor positioned within the housing. In some
instances, the surgical tool is part of an image-guided system.
In one embodiment, a method of selecting implant parameters
comprises: introducing one or more sensors adjacent to an
anatomical feature; monitoring a motion sequence of the anatomical
feature with the one or more sensors; analyzing the monitored
motion sequence of the anatomical feature to detect a problem in
the motion sequence of the anatomical feature; and determining a
parameter for an implant for at least partially correcting the
problem in the motion sequence of the anatomical feature. In some
instances, the method further comprises monitoring the motion
sequence of the anatomical feature with the one or more sensors
after implantation of the implant. In some instances, the method
further comprises: analyzing the monitored motion sequence of the
anatomical feature after implantation of the implant to detect a
remaining problem in the motion sequence of the anatomical feature;
and determining a modification of at least one parameter of the
implant to at least partially correct the remaining problem in the
motion sequence of the anatomical feature.
In some instances, monitoring the motion sequence comprises
tracking a position of the one or more sensors. In some instances,
monitoring the motion sequence comprises tracking a position of the
one or more sensors with respect to another of the one or more
sensors. In some instances, introducing one or more sensors
adjacent to an anatomical feature comprises implanting the one or
more sensors. In some instances, analyzing the monitored motion
sequence of the anatomical feature comprises utilizing a computer
system. In some instances, utilizing the computer system comprises
creating an animated model of the motion sequence. In some
instances, detecting the problem in the motion sequence comprises
comparing the animated model of the motion sequence to a
standardized model. In some instances, the anatomical feature is a
spinal joint. In some instances, introducing one or more sensors
comprises securing the one or more sensors to at least one
vertebra. In some instances, the method further comprises
identifying one or more spinal implants for at least partially
correcting the detected problem in the motion sequence of the
spinal joint. In some instances, at least one of the one or more
spinal implant is adjustable such that at least one parameter of
the spinal implant is modifiable. In some instances, the method
further comprises modifying the at least one parameter of the
adjustable spinal implant to substantially match the determined
parameter for correcting the problem in the motion sequence of the
anatomical feature.
In one embodiment, a method of selecting a spinal implant and its
parameters comprises: introducing a plurality of sensors adjacent
to a pair of vertebrae defining a spinal joint; monitoring a motion
sequence of the spinal joint with the plurality of sensors;
analyzing the monitored motion sequence of the vertebrae to detect
an initial problem in the motion sequence of the spinal joint;
determining a parameter for an implant for correcting the initial
problem in the motion sequence of the spinal joint; identifying at
least one spinal implant with the parameter for correcting the
initial problem in the motion sequence of the spinal joint.
In some instances, the method further comprises monitoring the
motion sequence of the spinal joint after implantation of a spinal
implant with the parameter for correcting the problem in the motion
sequence of the joint to detect a remaining problem in the motion
sequence of the spinal joint. In some instances, monitoring the
motion sequence of the spinal joint after implantation comprises
monitoring the motion sequence with at least one sensor positioned
within the spinal implant. In some instances, monitoring the motion
sequence of the spinal joint after implantation comprises
monitoring the motion sequence with the plurality of sensors. In
some instances, the method further comprises determining a factor
for an implant for correcting the remaining problem in the motion
sequence of the spinal joint; and identifying at least one spinal
implant with the factor for correcting the remaining problem in the
motion sequence of the spinal joint. In some instances, identifying
the at least one spinal implant with the factor for correcting the
remaining problem comprises identifying a modification to the
spinal implant with the parameter for correcting the initial
problem in the motion sequence of the spinal joint.
In one embodiment, a method of detecting implant loosening
comprises: providing an implant for fixedly engaging with an
anatomical feature of a patient, the implant having a first sensor
secured thereto; tracking a first motion pattern of the first
sensor; tracking a second motion pattern of a second sensor secured
to the anatomical feature; determining a relative motion between
the first sensor and the second sensor based on the first and
second motion patterns; and identifying implant loosening by
analyzing the relative motion between the first sensor and the
second sensor.
In some instances, identifying implant loosening comprises
identifying differences between the first motion pattern and the
second motion pattern. In some instances, a magnitude in the
differences between the first motion pattern and the second motion
pattern is indicative of the degree of loosening. In some
instances, determining a relative motion between the first and
second sensors comprising monitoring the relative angle of the
first sensor to the second sensor. In some instances, identifying
implant loosening comprises identifying a change in the relative
angle of the first sensor to the second sensor indicative of
implant loosening. In some instances, the implant is for fixedly
engaging with a bone. In some instances, the first sensor secured
to the implant is embedded within the implant. In some instances,
the implant is a spinal prosthetic. In some instances, the implant
is a fixation device. In some instances, the implant is configured
for insertion into an intramedullary canal of a long bone. In some
instances, the first and second sensors comprise
accelerometers.
In one embodiment, a method of detecting implant loosening
comprises: inserting a first sensor into a bone structure; securing
the first sensor in a fixed position with respect to the bone
structure; engaging an implant with at least a portion of the bone
structure, the implant having a second sensor positioned therein;
securing the implant with the portion of the bone structure such
that the second sensor is substantially fixed with respect to the
bone structure and the first sensor; and monitoring the position of
the second sensor with respect to the first sensor to identify
implant loosening.
In some instances, monitoring the position of the second sensor
with respect to the first sensor comprises monitoring the relative
angle of the second sensor to the first sensor. In some instances,
a change in the angle between the second sensor and first sensor is
indicative of implant loosening. In some instances, monitoring the
position of the second sensor with respect to the first sensor
comprises monitoring motion patterns of the first and second
sensors. In some instances, a difference between the motion
patterns of the first and second sensors is indicative of implant
loosening. In some instances, inserting the first sensor into the
bone structure comprises engaging a housing of the first sensor
with a vertebra. In some instances, engaging the implant with at
least a portion of the bone structure comprises inserting a spinal
implant. In some instances, the steps of inserting and securing the
first sensor comprise positioning the first sensor within a portion
of a long bone. In some instances, engaging the implant with at
least a portion of the bone structure comprises inserting an
elongated implant into an intramedullary canal of the long
bone.
The foregoing outlines features of several embodiments so that
those skilled in the art may better understand the aspects of the
present disclosure. Those skilled in the art should appreciate that
they may readily use the present disclosure as a basis for
designing or modifying other processes and structures for carrying
out the same purposes and/or achieving the same advantages of the
embodiments introduced herein. Those skilled in the art should also
realize that such equivalent constructions do not depart from the
spirit and scope of the present disclosure, and that they may make
various changes, substitutions and alterations herein without
departing from the spirit and scope of the present disclosure.
Further, while numerous embodiments have been described it is fully
contemplated that steps from various methods may be combined and
components from various devices and systems may be combined, even
if not explicitly described herein.
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